This document summarizes a proposed algorithm for reducing mixed noise in hyperspectral imagery. Hyperspectral images capture information across the electromagnetic spectrum and can be represented as three-dimensional tensors. The proposed method uses tensor decomposition and an improved K-SVD algorithm to adaptively detect and remove Gaussian noise, impulse noise, and mixtures of these from hyperspectral data. It formulates the noise removal problem using a weighted regularization approach and solves related optimization problems using techniques like singular value decomposition. The goal is to separate noise and noise-free components to reconstruct a cleaned hyperspectral image tensor.
Flame and smoke estimation for fire detection in videos based on optical flow...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Sub-windowed laser speckle image velocimetry by fast fourier transform technique
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame- single-exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed to obtain the velocity information with vector map analysis precisely.
Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of the spectral information measured on a specific region or object using an airborne or satellite device. Hyperspectral imaging has become an active field of research recently. One way of analysing such data is through clustering. However, due to the high dimensionality of the data and the small distance between the different material signatures, clustering such a data is a challenging task.In this paper, we empirically compared five clustering techniques in different hyperspectral data sets. The considered clustering techniques are K-means, K-medoids, fuzzy Cmeans, hierarchical, and density-based spatial clustering of applications with noise. Four data sets are used to achieve this purpose which is Botswana, Kennedy space centre, Pavia, and Pavia University. Beside the accuracy, we adopted four more similarity measures: Rand statistics, Jaccard coefficient, Fowlkes-Mallows index, and Hubert index. According to accuracy, we found that fuzzy C-means clustering is doing better on Botswana and Pavia data sets, K-means and K-medoids are giving better results on Kennedy space centre data set, and for Pavia University the hierarchical clustering is better
Flame and smoke estimation for fire detection in videos based on optical flow...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Sub-windowed laser speckle image velocimetry by fast fourier transform technique
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame- single-exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed to obtain the velocity information with vector map analysis precisely.
Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of the spectral information measured on a specific region or object using an airborne or satellite device. Hyperspectral imaging has become an active field of research recently. One way of analysing such data is through clustering. However, due to the high dimensionality of the data and the small distance between the different material signatures, clustering such a data is a challenging task.In this paper, we empirically compared five clustering techniques in different hyperspectral data sets. The considered clustering techniques are K-means, K-medoids, fuzzy Cmeans, hierarchical, and density-based spatial clustering of applications with noise. Four data sets are used to achieve this purpose which is Botswana, Kennedy space centre, Pavia, and Pavia University. Beside the accuracy, we adopted four more similarity measures: Rand statistics, Jaccard coefficient, Fowlkes-Mallows index, and Hubert index. According to accuracy, we found that fuzzy C-means clustering is doing better on Botswana and Pavia data sets, K-means and K-medoids are giving better results on Kennedy space centre data set, and for Pavia University the hierarchical clustering is better
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
Clustered Compressive Sensingbased Image Denoising Using Bayesian Frameworkcsandit
This paper provides a compressive sensing (CS) method of denoising images using Bayesian
framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
Design and Development of a Shortwave near Infrared Spectroscopy using NIR LE...IJECEIAES
Near infrared (NIR) spectroscopic technology has been getting more attention in various fields. The development of a low cost NIR spectroscopy is crucial to reduce the financial barriers so that more NIR spectroscopic applications will be investigated and developed by means of the NIR spectroscopic technology. This study proposes an alternative to measure shortwave NIR spectrum using one collimating lens, two slits, one NIR transmission grating, one linear array sensor, and one microcontroller. Five high precision narrow bands NIR light emitting diodes (LEDs) were used to calibrate the proposed spectroscopy. The effects of the proposed two slits design, the distance between the grating and linear array sensor, and three different regression models were investigated. The accuracy of the proposed design was cross-validated using leave-one-out cross-validation. Results show that the proposed two slits design was able to eliminate unwanted signals substantially, and the cross-validation was able to estimate the best model with root mean squared error of cross-validation of 3.8932nm. Findings indicate that the cross-validation approach is a good approach to estimate the final model without over-fitting, and the proposed shortwave NIR spectroscopy was able to estimate the peak value of the acquired spectrum from NIR LEDs with RMSE of 1.1616nm.
Fusion of Multispectral And Full Polarimetric SAR Images In NSST DomainCSCJournals
Polarimetric SAR (POLSAR) and multispectral images provide different characteristics of the imaged objects. Multispectral provides information about surface material while POLSAR provides information about geometrical and physical properties of the objects. Merging both should resolve many of object recognition problems that exist when they are used separately. Through this paper, we propose a new scheme for image fusion of full polarization radar image (POLSAR) with multispectral optical satellite image (Egyptsat). The proposed scheme is based on Non-Subsampled Shearlet Transform (NSST) and multi-channel Pulse Coupled Neural Network (m-PCNN). We use NSST to decompose images into low frequency and band-pass sub- band coefficients. With respect to low frequency coefficients, a fusion rule is proposed based on local energy and dispersion index. In respect of sub-band coefficients, m-PCNN is used to guide how the fused sub-band coefficients are calculated using image textural information.
The proposed method is applied on three batches of Egyptsat (Red-Green-infra-red) and radarsat2 (C-band full-polarimetric HH-HV and VV-polarization) images. The batches are selected to react differently with different polarization. Visual assessment of the obtained fused image gives excellent information on clarity and delineation of different objects. Quantitative evaluations show the proposed method can superior the other data fusion methods.
Comparitive analysis of doa and beamforming algorithms for smart antenna systemseSAT Journals
Abstract This paper revolves around the implementation of Direction of arrival and Adaptive beam-forming algorithms for Smart Antenna Systems. This paper also investigates the implementation of algorithms on various planner array geometries viz. circular and rectangular. Music algorithm is primarily finds the possible location of desired user and adaptive beam-forming algorithms such as LMS, RLS and CMA algorithms adapts the weights of the array. DOA estimation gives the maximum peak of spectrum with respect to angle of arrival where the desired user is supposed to exist. After DOA estimation weights of array antenna are changed with the changing received signal. This methodology is called as Spectral estimation, which allows the antenna pattern to steer in desired direction estimated by DOA and simultaneously null out the interfering signals. Rate of convergence is the major criterion for comparison for adaptive beam-forming algorithms. Keywords: DOA, MUSIC, LMS, RLS, CMA, SAS.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of various parameters by comparison of conventional pitc...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
Clustered Compressive Sensingbased Image Denoising Using Bayesian Frameworkcsandit
This paper provides a compressive sensing (CS) method of denoising images using Bayesian
framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
Design and Development of a Shortwave near Infrared Spectroscopy using NIR LE...IJECEIAES
Near infrared (NIR) spectroscopic technology has been getting more attention in various fields. The development of a low cost NIR spectroscopy is crucial to reduce the financial barriers so that more NIR spectroscopic applications will be investigated and developed by means of the NIR spectroscopic technology. This study proposes an alternative to measure shortwave NIR spectrum using one collimating lens, two slits, one NIR transmission grating, one linear array sensor, and one microcontroller. Five high precision narrow bands NIR light emitting diodes (LEDs) were used to calibrate the proposed spectroscopy. The effects of the proposed two slits design, the distance between the grating and linear array sensor, and three different regression models were investigated. The accuracy of the proposed design was cross-validated using leave-one-out cross-validation. Results show that the proposed two slits design was able to eliminate unwanted signals substantially, and the cross-validation was able to estimate the best model with root mean squared error of cross-validation of 3.8932nm. Findings indicate that the cross-validation approach is a good approach to estimate the final model without over-fitting, and the proposed shortwave NIR spectroscopy was able to estimate the peak value of the acquired spectrum from NIR LEDs with RMSE of 1.1616nm.
Fusion of Multispectral And Full Polarimetric SAR Images In NSST DomainCSCJournals
Polarimetric SAR (POLSAR) and multispectral images provide different characteristics of the imaged objects. Multispectral provides information about surface material while POLSAR provides information about geometrical and physical properties of the objects. Merging both should resolve many of object recognition problems that exist when they are used separately. Through this paper, we propose a new scheme for image fusion of full polarization radar image (POLSAR) with multispectral optical satellite image (Egyptsat). The proposed scheme is based on Non-Subsampled Shearlet Transform (NSST) and multi-channel Pulse Coupled Neural Network (m-PCNN). We use NSST to decompose images into low frequency and band-pass sub- band coefficients. With respect to low frequency coefficients, a fusion rule is proposed based on local energy and dispersion index. In respect of sub-band coefficients, m-PCNN is used to guide how the fused sub-band coefficients are calculated using image textural information.
The proposed method is applied on three batches of Egyptsat (Red-Green-infra-red) and radarsat2 (C-band full-polarimetric HH-HV and VV-polarization) images. The batches are selected to react differently with different polarization. Visual assessment of the obtained fused image gives excellent information on clarity and delineation of different objects. Quantitative evaluations show the proposed method can superior the other data fusion methods.
Comparitive analysis of doa and beamforming algorithms for smart antenna systemseSAT Journals
Abstract This paper revolves around the implementation of Direction of arrival and Adaptive beam-forming algorithms for Smart Antenna Systems. This paper also investigates the implementation of algorithms on various planner array geometries viz. circular and rectangular. Music algorithm is primarily finds the possible location of desired user and adaptive beam-forming algorithms such as LMS, RLS and CMA algorithms adapts the weights of the array. DOA estimation gives the maximum peak of spectrum with respect to angle of arrival where the desired user is supposed to exist. After DOA estimation weights of array antenna are changed with the changing received signal. This methodology is called as Spectral estimation, which allows the antenna pattern to steer in desired direction estimated by DOA and simultaneously null out the interfering signals. Rate of convergence is the major criterion for comparison for adaptive beam-forming algorithms. Keywords: DOA, MUSIC, LMS, RLS, CMA, SAS.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of various parameters by comparison of conventional pitc...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A survey on data security in cloud computing issues and mitigation techniqueseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Spatio temporal modeling of snow flake crystals using packard’s cellular auto...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Reuse of inorganic sludge as a coagulant on colloidal suspension removal in r...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Review of Use of Nonlocal Spectral – Spatial Structured Sparse Representation...IJERA Editor
Noise reduction may be a vigorous analysis area in image method due to its importance in up the quality of image for object detection and classification. Throughout this paper, we've got a bent to develop a skinny illustration based noise reduction methodology for the hyperspectral imaging , that depends on the thought that the non-noise part in associate discovered signal is sparsely rotten over a redundant lexicon whereas the noise part does not have this property. The foremost contribution of the paper is at intervals the introduction of nonlocal similarity and spectral-spatial structure of hyperspectral imaging into skinny illustration. Non-locality suggests that the self-similarity of image, by that a full image is partitioned into some groups containing similar patches. The similar patches in each cluster unit sparsely delineate with a shared set of atoms throughout a lexicon making true signal and noise extra merely separated. Sparse illustration with spectral-spatial structure can exploit spectral and spatial joint correlations of hyperspectral imaging by victimization 3D blocks rather than 2-D patches for skinny secret writing, which collectively makes true signal and noise extra distinguished. Moreover, hyperspectral imaging has every signal-independent and signal-dependent noises, thus a mixed Poisson and man of science noise model is used. In order to create skinny illustration be insensitive to various noise distribution in numerous blocks, a variance-fitting transformation (VFT) is used to create their variance comparable, the advantages of the projected ways unit valid on every artificial and real hyperspectral remote sensing data sets.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Medical image analysis and processing using a dual transformeSAT Journals
Abstract The demand for images in medical field has increased drastically over the years. The need for reducing the storage space has resulted in image compression. This paper presents a dual transform for medical image compression algorithm. The experimental results determines how the compression ratio (CR), peak signal to noise ratio (PSNR) and SNR (signal to noise ratio) of different compression algorithms responds to dual transform algorithm. Keywords: DCT, SPIHT, Haar Wavelet, Linear approximation transform, image compression, Singular Value Decomposition (SVD).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image resolution enhancement using blind techniqueeSAT Journals
Abstract Image resolution enhancement (IRE) is the process of manipulating a set of low quality images and produce high quality and high resolution images. The two groups of techniques to increase the apparent resolution of the imaging system are Blind deconvolution (BD) and Super-resolution (SR).Most publications on BD/SR are non-blind, i.e., do not explicitly consider blur identification during the reconstruction procedure. This technical paper, we focuses on various methods of super resolution ,blind deconvolution and unifying blind approach to the blind deconvolution and super resolution problem i.e., methods that combine blur identification and image restoration into a single procedure, e.g. alternating minimization (AM). Keywords: Blind Decovolution (BD), Super Resolution (SR), Alternating Minimization (AM)
Image fusion is a technique used to integrate a highresolution
panchromatic image with multispectral low-resolution
image to produce a multispectral high-resolution image, that
contains both the spatial information of the panchromatic highresolution
image and the color information of the multispectral
image .Although an increasing number of high-resolution images
are available along with sensor technology development, the
process of image fusion is still a popular and important method to
interpret the image data for obtaining a more suitable image for a
variety of applications, like visual interpretation and digital
classification. To get the complete information from the single
image we need to have a method to fuse the images. In the current
paper we are going to propose a method that uses hybrid of
wavelets for Image fusion.
Abstract: Primarily due to the progresses in super resolution imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a image segmentation based on colour features with K-means clustering. The entire work is divided into two stages. First enhancement of color separation of satellite image using de correlation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudoedges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, demonstrate that the proposed methodology can adequately lessen the pseudoedges of the aggregate variety regularization in the level.
Image Denoising Using Earth Mover's Distance and Local HistogramsCSCJournals
In this paper an adaptive range and domain filtering is presented. In the proposed method local histograms are computed to tune the range and domain extensions of bilateral filter. Noise histogram is estimated to measure the noise level at each pixel in the noisy image. The extensions of range and domain filters are determined based on pixel noise level. Experimental results show that the proposed method effectively removes the noise while preserves the details. The proposed method performs better than bilateral filter and restored test images have higher PSNR than those obtained by applying popular Bayesshrink wavelet denoising method.
Reduction of Azimuth Uncertainties in SAR Images Using Selective RestorationIJTET Journal
Abstract— A framework is proposed for reduction of azimuth uncertainty space borne strip map synthetic aperture radar (SAR) images. In this paper, the azimuth uncertainty in SAR images is identified by using a local average SAR image, system parameter, and a distinct metric derived from azimuth antenna pattern. The distinct metric helps isolate targets lying at locations of uncertainty. The method for restoration of uncertainty regions is selected on the basis of the size of uncertainty regions. A compressive imaging technique is engaged to bring back isolated ambiguity regions (smaller regions of interrelated pixels), clustered regions (relatively bigger regions of interrelated pixels) are filled by using exemplar-based in-painting. The recreation results on a real Terra SAR-X data set established that the proposed method can effectively remove azimuth uncertainties and enhance SAR image quality.
Modified adaptive bilateral filter for image contrast enhancementeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Survey of local binary pattern for fire & smoke using wavelet decompositioneSAT Journals
Abstract
Current Automatic Smoke Detection Mechanism’s, i.e. Fire Alarms, include sensor technologies, which are unreliable, as the
alarm can go off, even though there is no fire or smoke. Using image processing a quite accurate system for smoke detection
and amount of smoke generated, i.e. seriousness of the condition when there is a fire can be estimated. So, a pixel level analysis
is required.
Keywords: Smoke Detection, Image Segmentation, Color Space Model, Fuzzy Inference System.
An Ultrasound Image Despeckling Approach Based on Principle Component AnalysisCSCJournals
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
Smart Antenna is a device with signal processing
capability combining multiple antenna elements to optimize its
radiation and reception patterns as per designed specifications.
Smart antennas basically comprise of two functionalities, i.e.,
Direction of Arrival and Beamforming. This paper explains the
estimation of Direction of Arrival using MLM method and a
novel approach called MUltiple Signal Classification which takes
advantage of orthogonal property and performs subspace
computation. With a comparative study of both the algorithms,
we shall prove the advantages of MUltiple Signal Classification
over the MLM method with the aid of MATLAB
This is the project report for my internship at HBCSE-TIFR. The project describes a low-cost method for analysing the spectrum of LEDs and determining the wavelength.
Ijri ece-01-02 image enhancement aided denoising using dual tree complex wave...Ijripublishers Ijri
This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform , lanczos interpolator and edge preserving smoothing filters. Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized.
Keywords: dual-tree complex wavelet transform (DTWCT), lanczos interpolator, edge preserving smoothing filters.
Similar to Hyperspectral image mixed noise reduction based on improved k svd algorithm (20)
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Runway Orientation Based on the Wind Rose Diagram.pptx
Hyperspectral image mixed noise reduction based on improved k svd algorithm
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 834
HYPERSPECTRAL IMAGE MIXED NOISE REDUCTION BASED ON
IMPROVED K-SVD ALGORITHM
S. Shajun Nisha1
, P. Thazneem Fazila2
, S. Kother Mohideen3
1
Professor and Head, Department of Computer Science (PG), Sadakathullah Appa College, Tamil Nadu, India
2
Student, Computer Science and Engineering, National College of Engineering, Tamil Nadu, India
3
Professor, Computer Science and Engineering, National College of Engineering, Tamil Nadu, India
Abstract
We propose an algorithm for mixed noise reduction in Hyperspectral Imagery (HSI). The hyperspectral data cube is considered
as a three order tensor. These tensors give a clear view about both spatial and spectral modes. The HSI provides ample spectral
information to identify and distinguish spectrally unique materials, thus they are spectrally over determined. Tensor
representation is three ordered thus can simultaneously deal with the two spatial dimensions and one spectral dimension of HSI to
achieve a satisfying noise reduction performance. The image analysis application like Classification, unmixing, subpixel mapping
and target detection are performed in a very accurate manner due to the development of hyperspectral remote sensing technology
as it provides large amount of spatial and spectral information. This entire denoising process is based on the K-SVD denoising
algorithm. This method of denoising can efficiently remove a variety of mixed or single noise by applying sparse regularization of
small image patches. It also maintains the image texture in a clear manner. The learned dictionary used clearly helps in
removing the noise. Our work involved in minimizing model to remove mixed noise such as Impulse noise, Gaussian-Gaussian
mixture and Gaussian-Impulse noise from the HSI data. The weighted rank-one approximation problem is solved using a new
iterative scheme and the low rank approximation can be obtained by singular value decomposition(SVD).A new weighting data
fidelity function which is much easier to optimize is used which has the same minimizer as the original likelihood function. The
weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the
different estimated noise parameters.
Keywords: Key Hyperspectral image, K-SVD algorithm, low rank approximation, Gaussian noise, Impulse noise,
mixed noise
--------------------------------------------------------------------***--------------------------------------------------------------------
1. INTRODUCTION
The „Hyper‟ means „over‟ and refers to a large number of
measured wavelength bands. The acquired HSI images are
mostly corrupted by radiometric noise such as calibration
error, atmospheric scattering, sensor noise, photon noise and
absorption. There are two category of noise which affects
HSI images they are the random noise and fixed–pattern
noise. Fixed pattern noise is mostly due to calibration can be
mitigated with suitable methods. In contrast, random noise
due to its stochastic nature cannot be removed entirely. The
random noise in HSI is the additive model, which is
assumed to be white, Gaussian and independent-from-
signal.
Some of the traditional denoising algorithms are channel by
channel, singular value decomposition (SVD), Wiener and
wavelet filters. However these do not deal with the spatial
and spectral information simultaneously and may lead to
loss of the inter-dimensional information. In recent years,
some algorithm combines the spatial and spectral
information for HSI noise reduction. The algorithm like
hybrid spatial spectral derivative domain wavelet shrinkage
noise reduction (HSSNR) approach and spectral- spatial
adaptive total variation model considers both spatial and
spectral information in hyperspectral image for denoising.
The spatial and spectral information are completely
preserved in multilinear algebra since the HSI data cube can
be considered as a three order tensor. The multidimensional
filtering based on tucker tensor decomposition is an example
of such approach. One of the Tucker based noise reduction
is the multidimensional Wiener filtering (MWF) algorithm
[13] which achieves simultaneous improvement in the image
quality and classification accuracy. However this application
may lead to information compression and loss of spatial
details. In the rank-1 tensor decomposition (R1TD)
algorithm [24], the input data cube is considered as three
order tensor. Subsequently, it sorts the eigen values
generated by tensor decomposition and then extract the
signal-dominant component from the observed HSI data
cube. These signal-dominant components are extracted from
the data cube by sorting the weights of the rank-1tensor, and
then they are reconstructed to produce the noise free image.
In this work, we propose a general framework to adaptively
detect and remove noise of different type, including
Gaussian noise, impulse noise and more importantly, their
mixture in the HSI data. The HSI data is considered as a
three order tensor which treats both spatial and spectral
modes of the given image. The image undergoes tensor
decomposition; later the modified K-SVD algorithm is
applied to the tensors. The regularized maximum likelihood
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 835
estimation (MLE) is modified with new function along with
additional variable, since the original likelihood functional
related to mixed noise is not easy to be optimized compared
with the functional for a single Gaussian noise. This new
functional has the same global minimizer (or maximizer) as
the original likelihood functional and is easier to be
optimized. The weighting functions play the role of noise
detectors by minimizing the new functional, we obtain some
weighted norms models. We also integrate this with sparsity
representation thus our model can well restore images and
textures corrupted by mixed noise. A new iterative scheme is
given to solve the weighted rank-one approximation
problem arisen from the proposed model and the low rank
approximation can be obtained by singular value
decomposition (SVD). Our method integrates four steps as
follows sparse coding followed by dictionary learning,
image reconstruction, noise clustering (detection), and
parameters estimation. Each step needs to perform a
minimization problem. Then these optimized tensors are
separated as noise free tensor and noisy tensor. The noise
free tensors are then combined to reconstruct the noise free
image. The reconstruction is same as reverse of the tensor
decomposition.
The remainder of the paper is organized as follows. Section2
deals with brief review about tensor and its operation.
Section 3 proposed method and section 4 provides the
experimental results. Finally, section 5 concludes this study.
2. BRIEF REVIEW ABOUT HSI AND TENSORS
2.1 Imaging Techniques
RGB camera image is a type of multispectral image that
uses the light intensity at three specific wavelengths: red,
green, and blue, thus creating an image in the visible region.
Thus depending on the number of spectral bands and
wavelengths measured, an image is classified as a
multispectral image when several wavelengths are
measured. A hyperspectral image is complete wavelength
region, i.e., the whole spectrum, is measured for each spatial
point. For example, The Fig 1 compares the optical
information obtained by black and white camera, RGB
cameras, and hyperspectral cameras.
Fig-1: Difference in Imaging
2.2 Imaging Spectrometer
The instrument imaging spectrometers is used to produce
the Hyperspectral images. These complex sensors have
involved the convergence of two distinct but related
technologies: spectroscopy and the remote imaging of Earth
and planetary surfaces.
The study of light that is emitted by or reflected from
materials and its variation in energy with wavelength is
referred as Spectroscopy.
Fig-2: Imaging Spectrometer
In the field of optical remote sensing, spectroscopy deals
with the spectrum of sunlight that is diffusely reflected
(scattered) by materials at the Earth‟s surface. The ground-
based or laboratory measurements of the light reflected from
a test material are measured using the instrument like
spectrometer (or spectrodiameter). The spectrometer
consists of an optical dispersing element such as a grating or
prism which splits this light into many narrow, adjacent
wavelength bands. This energy in each band is measured by
a separate detector. Spectrometers can make spectral
measurements of bands as narrow as 0.01 micrometers over
a wide wavelength range, typically at least 0.4 to 2.4
micrometers by using hundreds or even thousands of
detectors.
2.3 Tensors
A tensor is defined as a multidimensional array and
represented as 𝐴 ∈ 𝑅 𝐿1×𝐿2×……..×𝐿 𝑁 . Tensor is the higher-order
equivalent of the vector (one-order tensor) and a matrix
(two-order tensor). In this study, the HSI data cube is a
three-order tensor 𝐴 ∈ 𝑅 𝐿1×𝐿2×𝐿3 . The modes 1 and 2 belong
to the spatial modes and mode 3 belongs to the spectral
mode. Taking each vector to be in different mode the outer
product of three vectors can be visualized as follows,
Fig-3: Outer product of three vectors forming a tensor
Mathematically, the outer product of three vectors a; b; c is
written as follows,
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 836
Fig-4: Tensor matricizationin three modes Tensor
matricization reorders tensor into a matrix. The n-mode
matricization of X belongs to RL1×L2×…×LN is matnX
belongs to RLi×(L1L2...Ln-1 Ln+1...LN) , which is the
ensemble of vectors in the n-mode obtained by keeping
index Li fixed and varying the other indices. The tensor
Matricization of three modes is shown in Fig 4.
3. METHODOLOGY
The given HSI is the input image to the system. This HSI
image is read and displayed. Then the HSI image processed
in the R1TD algorithm to provide the Rank-1 Tensor
profiles. With these profiles, we perform the Alternative
Least Square Algorithm to optimize the tensors. Then we
sort the tensors of higher order and reconstruct the noise free
image by combining signal dominant components.
3.1 HSI Image Reader
The Hyperspectral imaging (HSI) collects and process
information from across the electromagnetic spectrum. The
human eye sees visible light in three bands (red, blue,
green). Spectral imaging divides the spectrum into many
more bands. This technique of dividing images into bands
can be extended before can be extended beyond the
visibility. Hyperspectral sensors collect a set of images.
Each image is a range of the electromagnetic spectrum
known as a spectral band. Then these images are combined
together to form a three dimensional hyperspectral data
cube. This module is designed to read and visualize the HSI
images.
The HSI data is considered as multiple images combined as
a cube. Thus we have view each image in a well furnished
manner. Each image ahs slice of images of different colors.
This slice of image is not taken as a single color image for
the calculation instead it is taken as whole cube called
tensors.
Here O as the observed HSI data cube. This O consist both
the noise free image S and the additive noise component N.
By extending the classic two-dimensional additive noise
model, the tensorial formulation is,
O=S+N (1)
In this model, the noise is considered to be white, Gaussian
and independent from signal.
3.2 Tensor Decomposition
New tensor decomposition is developed which jointly treats
both the spatial and spectral modes. The R1TD algorithm is
applied to the HSI data input which takes into account both
the spatial and spectral information. The tensor
decomposition is of the form CANDECOMP/PARAFAC
decomposition (Canical decomposition and parallel factor
decomposition).
According to the definitions of the rank 1 tensor and vector
outer product, tensor O ∈ RL1×L2×L3 can be represented with
the rank-1 tensor decomposition model:
O = λrUr ∘ Vr ∘ Wr
M
r=1 (2)
Where Ur ∈ RL1 , Vr ∈ RL2 and Wr ∈ RL3 are vectors on
three modes (one spectral and two spatial).Here M is
represented as the number of rank-1 tensors used to restore
whole tensor O, λr is the weight value. There is no straight
forward solution to M. As the rank of a three order tensor is
equivalent to the minimal number of triads necessary to
describe the tensor.
3.3. Improved K-SVD Algorithm
The K-SVD algorithm is solved with four sub-minimization
problem.
3.3.1 Sparse Coding and Dictionary Learning
The first minimization problem is
(𝛼 𝜈+1
, 𝐷 𝜈+1
) = arg min 𝛼,𝐷 𝒥(𝛼, 𝐷, 𝑓𝜈, 𝑢 𝜈 , Θ 𝜈 ) (3)
The problem is split into two convex sub problems
corresponding to the so-called sparse coding step and the
dictionary learning step, respectively. Let ν1 be an inner
iteration number, then 𝛼 𝜈+1, and 𝐷𝜈+1can be obtained by
solving the following two minimization problems
iteratively:
Sparse Coding (Conjugated OMP)
𝛼 𝜈1+1
= arg min 𝛼 𝒥(𝛼, 𝐷, 𝑓𝜈, 𝑢 𝜈, Θ 𝜈 )
= 𝑎𝑟𝑔 min
𝛼
𝜆
2
𝑊𝑖 𝐷 𝜈1 𝛼.,𝑖 − 𝑊𝑖 𝑅𝑖 𝑓 𝜈
2
2
+𝑁
𝑖=1
𝑖=1𝑁𝜇𝑖𝛼.,𝑖0 (4)
Where 𝑊𝑖the diagonal matrix is whose diagonal elements
are𝑅𝑖𝜔 .
Dictionary Learning: (Modified K-SVD)
The original K-SVD is modified by the non-uniform
weights. We denote
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 837
𝑊 = 𝑅1𝜔 … 𝑅 𝑁𝜔 , 𝑋 = (𝑅1 𝑓 … 𝑅 𝑁 𝑓) (5)
Then
𝐷 𝜈1+1
= arg min 𝐷, 𝑑 𝑘 2=1 𝑊 ∘ (𝐷𝛼 𝜈1+1
− 𝑋 𝜈
) 𝐹
2
(6)
Similar to the K-SVD learning algorithm [13] is the natural
approach to minimize each atom 𝑑 𝑘 as follows:
𝑑 𝑘
𝜈1+1
= arg min 𝑑 𝑘 2=1 𝑊 ∘ (𝐸 𝑘
− 𝑑 𝑘 𝛼 𝑘,.
𝜈1+1
)
𝐹
2
(7)
In the above, 𝐸 𝑘
≜ 𝑋 𝜈
− 𝑑𝑙
𝜈1
𝛼𝑙,.
𝜈1+1𝐾
𝑙=1,𝑙≠𝑘 . This is the
weighted approximation problem. This is rectified by an
iterative algorithm [22] as follows
𝑑 𝑘
𝜈1+1
= arg min 𝑑 𝑘 2=1 𝑊 ∘ 𝐸 𝑘
− 𝑑 𝑘 𝛼 𝑘,.
𝜈1+1
+
𝑑𝑘𝜈1𝛼𝑘,.𝜈1+1− 𝑑𝑘𝛼𝑘,.𝐹2………(8)
via SVD. This algorithm cannot be used for the unweighted
case. Thus we solve the minimization problem was:
𝑑 𝑘
𝜈1+1
= arg min 𝑑 𝑘 2=1 𝑊 ∘ 𝐸 𝑘
− 𝑑 𝑘 𝛼 𝑘,.
𝜈1+1
+
𝜏𝑘𝑑𝑘𝜈1𝛼𝑘,.𝜈1+1− 𝜏𝑘𝑑𝑘𝛼𝑘,.𝐹2 (9)
to update the atoms, where𝜏 𝑘 = (𝑑 𝑘
𝜈1
) 𝑇 𝑊 𝑖
𝑁
𝑖=1
𝑁
𝑑 𝑘
𝜈1
. Thus
the modified scheme reduces the original K-SVD algorithm
when all weights are the same.
The modified K-SVD algorithm for weighted norm are as
follows:
Select the index set of patches SkThat use atom dk
𝑆𝑘 = 𝑖: 𝛼 𝑘,𝑖
𝜈1+1
≠ 0, 𝑖 ≤ 𝑖 ≤ 𝑁 . (10)
Let 𝜏 𝑘 = (𝑑 𝑘
𝜈1
) 𝑇 𝑊 𝑖
𝑁
𝑖=1
𝑁
𝑑 𝑘
𝜈1
, the residual is calculated for
each image patch
𝑒𝑖
∼𝑘
= 𝑊𝑖 𝑅𝑖 𝑓 𝜈
− 𝐷 𝜈1 𝛼.,𝑖
𝜈1+1
+ 𝜏 𝑘 𝑑 𝑘
𝜈1
𝛼 𝑘,𝑖
𝜈1+1
(11)
Set 𝐸 𝑘
∈ ℝ 𝑛1 𝑛2× 𝑆 𝑘 with its columns being the 𝑒𝑖
∼𝑘
and
update 𝑑 𝑘
𝜈1+1
by minimizing
(𝑑 𝑘
𝜈1+1
, 𝛽∗
= 𝑎𝑟𝑔 min
𝑑 𝑘 2=1,𝛽
𝐸 𝑘
− 𝜏 𝑘 𝑑 𝑘 𝛽 𝑇
𝐹
2
(12)
Where 𝛽 ∈ ℝ 𝑆 𝑘 . The rank-one approximation problem can
be solved using SVD decomposition of 𝐸 𝑘
Then we replace 𝛼 𝑘,𝑖
𝜈1+1
, 𝑖 ∈ 𝑆𝑘 by relevant elements of β*.
In our experiment, the inner iteration number is chosen
as 𝜈1 = 10.
3.3.2 Reconstruction
This minimization problem is solved as follows, since 𝓙 is
quadratic with respect to f, thus
𝑓 𝜈+1
= 𝑑 𝜔 ∘ 𝜔 + 𝜆 𝑅𝑖
𝑇
𝑑((𝑅𝑖 𝜔) ∘
𝑁
𝑖=1
(𝑅𝑖 𝜔))𝑅𝑖)
−1
× 𝑑(𝜔 ∘ 𝜔)𝑔 + 𝜆 𝑅𝑖
𝑇
𝑑((𝑅𝑖 𝜔) ∘𝑁
𝑖=1 (𝑅𝑖 𝜔))𝑅𝑖 ×
𝐷 𝜈+1
𝛼.,𝑖
𝜈+1
(13)
Where 𝑑(𝜔 ∘ 𝜔) represents 𝑑𝑖𝑎𝑔 𝜔 ∘ 𝜔 and Ri is a
diagonal matrix. So the inverse matrix can be directly
obtained.
Noise Clustering (Exception Step): The solution to the
minimization problem is uυ+1
and it can be computed by
𝑢.,𝑙
𝜈+1
=
𝑟1
𝜈
𝜎1
𝜈 exp (−𝑇 𝑙)
𝑟 𝑠
𝜈
𝜎 𝑠
𝜈 exp (−𝑇𝑠)𝑀
𝑠=1
(14)
Parameter Estimation: The minimization for this step is
(15)
From equation
𝜕𝒥
𝜕Θ
= 0, we get the closed-form solution
of Θ 𝜈+1
:
(16)
3.4 Denoising
The signal dominant components are combined removing
the noise tensors. After the noise components are removed,
the noise free image is obtained by reconstructing the signal
dominant components.
The tensors are reconstructed to form the noise free HSI
data by the formula
S = λrUr ∘ Vr ∘ Wr
k
r=1 (17)
The value of K is the number of signal dominant tensors.
4. EXPERIMENTAL RESULTS
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 838
The proposed algorithm is applied in 3 set of HSI data. The
HIS cannot be taken as an image itself. The values are to be
plotted as an image for our visualization. Thus a set of
values of the received image is plotted as an image for our
visualization. The values are plotted as an image for the
original data and for the denoised data. The original values
are not plotted fully, only certain area shown for the
visualization for a clear idea of the HSI image. The three
images with their denoised output is shown in figure 5-7,
Fig- 5: HSI Data set 1 for HSI image visualization and the
Denoised data set 1
Fig-6: HSI Data set 2 for HSI image visualization and the
Denoised data set 2
Fig-7: HSI Data set 3 for HSI image visualization and the
Denoised data set 3
The effectiveness of the proposed algorithm is proved by
comparing the proposed model with several competitive
methods: Spectral-Spatial Adaptive Total Variation
(SSAHTV), Multidimensional Wiener Filtering (MWF) and
Rank-1 Tensor Decomposition (R1TD). In the algorithm
SSAHTV [15] and MWF [13] the correlation between the
spatial and spectral bands is not simultaneously considered
thus may lead to inter-dimensional information loss. The
application of tensor product may lead to information
compression and loss of spatial details. The R1TD [24]
provides clear view than that of the other two but it deals
with only Additive white and Gaussian noise. The proposed
algorithm deals mixed noise like impulse, Gaussian-
Gaussian, Gaussian-impulse. Also it provides a higher
PSNR than that of the existing system.
The PSNR is the ratio between the maximum possible
power of a signal and the power of corrupting noise that
affects the fidelity of its representation. The PSNR for the
Existing System is compared with the proposed algorithm in
the Table I. This comparison confirms that proposed method
is has higher values than that of the existing systems. Also
the existing system deals only with single noise whereas this
deals with mixed noise.
Table-1: PSNR comparison for the existing and proposed
system
Band
No.
MWF SSAHTV R1TD Improved K-
SVD
1 24.48 28.40 30.39 42.79
2 24.66 26.44 30.43 40.38
3 25.48 28.43 30.43 38.30
4 24.31 28.21 30.20 37.39
5 24.73 29.02 29.99 37.15
6 23.73 27.56 29.52 37.27
The graph is plotted with the PSNR values provided in the
table in chart-1.
Chart-1: Graph providing the comparison of existing and
the proposed system.
5. CONCLUSIONS
We provide a new algorithm to remove mixed noise using
the PDF. By combing the sparsity regularization and
dictionary learning techniques, a novel and efficient model
is designed to remove mixed noise such as impulse noise,
Gaussian-Gaussian mixture, and Gaussian plus impulse
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Special Issue: 07 |May-2014, Available @ http://www.ijret.org 839
noise. Also it considers both spatial and spectral views of
the hyperspectral image thus provide data quality in terms of
both visual inspection and image quality.
REFERENCES
[1] Acito, N., Diani, M., Corsini, G., 2010.
Hyperspectral signal subspace identification in the
presence of rare signal components. IEEE Trans.
Geosci. Remote Sens. 48 (4), 1940–1954.
[2] Acito, N., Diani, M., Corsini, G., 2011a. Signal-
dependent noise modeling and model parameter
estimation in hyperspectral images. IEEE Trans.
Geosci. Remote Sens. 49 (8), 2957–2971.
[3] Acito, N., Diani, M., Corsini, G., 2011b. Subspace-
based striping noise reduction in hyperspectral
images. IEEE Trans. Geosci. Remote Sens. 49 (4),
1325–1342. Akaike, H., 1974.
[4] Ana Rovi., 2010. Analysis of 2× 2 × 2 Tensors.
master‟s thesis
[5] M. Elad and M. Aharon, “Image denoising via
learned dictionaries and sparse representation,” in
Proc. IEEE Comput. Vis. Pattern Recognit.,Jun.
2006, pp. 895–900.
[6] M. Elad and M. Aharon, “Image denoising via sparse
and redundant representations over learned
dictionaries,” IEEE Trans. Image Process.,vol. 15,
no. 12, pp. 3736–3745, Dec. 2006.
[7] M. Aharon, M. Elad, and A. Bruckstein, “The K-
SVD: An algorithm for designing of overcomplete
dictionaries for sparse representations,”IEEE Trans.
Image Process., vol. 54, no. 11, pp. 4311–4322, Nov.
2006.
[8] J. Mairal, M. Elad, and G. Sapiro, “Sparse
representation for color image restoration,” IEEE
Trans. Image Process., vol. 17, no. 1, pp. 53–69, Jan.
2008.
[9] Bourennane, S., Fossati, C., Cailly, A., 2011.
Improvement of target-detection algorithms based on
adaptive three-dimensional filtering. IEEE Trans.
Geosci. Remote Sens. 49 (4), 1383–1395.
[10] Brett W. Bader and Tamara G. Kolda MATLAB
Tensor Classes for Fast Algorithm Prototyping
[11] Bro, R., Kiers, H.A.L., 2003. A new efficient method
for determining the number of components in
PARAFAC models. J. Chemometr. 17 (5), 274–
286Karami, A., Yazdi, M., Asli, A.Z., 2011. Noise
reduction of hyperspectral images using kernel non-
negative tucker decomposition. IEEE J. Sel. Top.
Signal Process. 5 (3), 487–493.
[12] Landgrebe, D., 2002. Hyperspectral image data
analysis. IEEE Signal Process. Mag. 19 (1), 17–28.
[13] Letexier, D., Bourennane, S., 2008. Noise removal
from hyperspectral images by multidimensional
filtering. IEEE Trans. Geosci. Remote Sens. 46 (7),
2061–2069.
[14] Othman, H., Qian, S.-E., 2006. Noise reduction of
hyperspectral imagery using hybrid spatial–spectral
derivative-domain wavelet shrinkage. IEEE Trans.
Geosci. Remote Sens. 44 (2), 397–408.
[15] Yuan, Q., Zhang, L., Shen, H., 2012. Hyperspectral
image denoising employing a spectral-spatial
adaptive total variation model. IEEE Trans. Geosci.
Remote Sens. 50 (10), 3660–3677.
[16] H. Wang and R. Haddad, “Adaptive median filters:
New algorithms and results,” IEEE Trans. Image
Process., vol. 4, no. 4, pp. 499–502, Apr.1995.
[17] Y. Xiao, T. Zeng, J. Yu, and M. K. Ng, “Restoration
of images corrupted by mixed Gaussian-impulse
noise via l1-l0 minimization,”Pattern Recognit., vol.
44, no. 8, pp. 1708–1720, Aug. 2011.
[18] E. Lopez-Rubio, “Restoration of images corrupted by
Gaussian and uniform impulsive noise,” Pattern
Recognit., vol. 43, no. 5,pp. 1835–1846, 2010.
[19] J. Liu, H. Huang, Z. Huan, and H. Zhang, “Adaptive
variational method for restoring color images with
high density impulse noise,” Int. J.Comput. Vis., vol.
90, no. 2, pp. 131–149, 2010
[20] J. Bilmes. (1997). A Gentle Tutorial On The Em
Algorithm And Its Application To Parameter
Estimation For Gaussian Mixture And Hidden
Markov Models Available:
Http://Citeseerx.Ist.Psu.Edu/Viewdoc/Summary?Doi
=10.1.1.28.613
[21] N. Srebro And T. Jaakkola, “Weighted Low-Rank
Approximations,” In Proc. 20th Int. Conf. Mach.
Learn., 2003, Pp. 720–727.
[22] S. Ko And Y. Lee, “Center Weighted Median Filters
And Their Applications To Image Enhancement,”
Ieee Trans. Circuits Syst., Vol. 38, No. 9, Pp.984–
993, Sep. 1991.
[23] Xian Guo A, Xin Huang A,⇑ , Liangpei Zhang A,
Lefei Zhang”Hyperspectral Image Noise Reduction
Based On Rank-1 Tensor Decomposition”, Isprs
Journal Of Photogrammetry And Remote Sensing 83
(2013) 50–63
BIOGRAPHIES
Prof.S.ShajunNisha has completed
M.Phil.(Computer science) and M.Tech
(Computer and Information Technology) in
Manonmaniam Sundaranar University,
Tirunelveli. She is a member of ISTE and
IEANG and her specialization is Image Mining.
P. Thazneem Fazila has finished her
B.Tech (IT) and doing her M.E.(computer
Science and Engineering) in National
college of engineering. She has attended so
many national and international seminars,
conferences and presented research papers.
Dr. S. Kother Mohideen has been working
as a Professor in the Department of
Computer Science and Engineering,
National College of Engineering,
Tirunelveli. He is also Research convenor
of R&D Department. He is obtained M.Tech degree and
Ph.D degree from Manonmaniam Sundraranar University,
Tiruneveli. He is the member of IEEE and IEANG.