Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
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.
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.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
WAVELET THRESHOLDING APPROACH FOR IMAGE DENOISINGIJNSA Journal
The original image corrupted by Gaussian noise is a long established problem in signal or image processing .This noise is removed by using wavelet thresholding by focused on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image denoising . For the first part, threshold is driven in a Bayesian technique to use probabilistic model of the image wavelet coefficients that are dependent on the higher order moments of generalized Gaussian distribution (GGD) in image processing applications. The proposed threshold is very simple. Experimental results show that the proposed method is called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image. It outperforms Donoho and Johnston Sure Shrink. The second part of the paper is attempt to claim on lossy compression can be used for image denoising .thus achieving the image compression & image denoising simultaneously. The parameter is choosing based on a criterion derived from Rissanen’s minimum description length (MDL) principle. Experiments show that this compression & denoise method does indeed remove noise significantly, especially for large noise power.
Spot Edge Detection in cDNA Microarray Images using Window based Bi-Dimension...idescitation
Ongoing Microarray is an increasingly playing a crucial role applied in the field
of medical and biological operations. The initiator of Microarray technology is M. Schena et
al. [1] and from past few years microarrays have begun to be used in many fields such as
biomedicine, mostly on cancer and Diabetic, and medical diagnoses. A Deoxyribonucleic
Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface,
such as glass, plastic or silicon chip forming an array. Processing of DNA microarray image
analysis includes three tasks: gridding, segmentation and intensity extraction and at the
stage of processing, the irregularities of shape and spot position which leads to generate
significant errors. This article presents a new spot edge detection method using Window
based Bi-dimensional Empirical Mode Decomposition. On separating spots form the
background area and to decreases the probability of errors and gives more accurate
information about the states of spots we are proposing a spot edge detection via WBEMD.
By using this method we can identify the spots with low density, which leads to increasing
the performance of cDNA microarray images.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
The growing trend of online image sharing and downloads today mandate the need for better encoding and
decoding scheme. This paper looks into this issue of image coding. Multiple Description Coding is an
encoding and decoding scheme that is specially designed in providing more error resilience for data
transmission. The main issue of Multiple Description Coding is the lossy transmission channels. This work
attempts to address the issue of re-constructing high quality image with the use of just one descriptor
rather than the conventional descriptor. This work compare the use of Type I quantizer and Type II
quantizer. We propose and compare 4 coders by examining the quality of re-constructed images. The 4
coders are namely JPEG HH (Horizontal Pixel Interleaving with Huffman Coding) model, JPEG HA
(Horizontal Pixel Interleaving with Arithmetic Encoding) model, JPEG VH (Vertical Pixel Interleaving
with Huffman Encoding) model, and JPEG VA (Vertical Pixel Interleaving with Arithmetic Encoding)
model. The findings suggest that the use of horizontal and vertical pixel interleavings do not affect the
results much. Whereas the choice of quantizer greatly affect its performance.
Mimo radar detection in compound gaussian clutter using orthogonal discrete f...ijma
This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for
Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed
orthogonal waveforms are designed considering the position and angle of the transmitting antenna when
viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized
Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better
detection performance in spikier Clutter.
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.
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.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
WAVELET THRESHOLDING APPROACH FOR IMAGE DENOISINGIJNSA Journal
The original image corrupted by Gaussian noise is a long established problem in signal or image processing .This noise is removed by using wavelet thresholding by focused on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image denoising . For the first part, threshold is driven in a Bayesian technique to use probabilistic model of the image wavelet coefficients that are dependent on the higher order moments of generalized Gaussian distribution (GGD) in image processing applications. The proposed threshold is very simple. Experimental results show that the proposed method is called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image. It outperforms Donoho and Johnston Sure Shrink. The second part of the paper is attempt to claim on lossy compression can be used for image denoising .thus achieving the image compression & image denoising simultaneously. The parameter is choosing based on a criterion derived from Rissanen’s minimum description length (MDL) principle. Experiments show that this compression & denoise method does indeed remove noise significantly, especially for large noise power.
Spot Edge Detection in cDNA Microarray Images using Window based Bi-Dimension...idescitation
Ongoing Microarray is an increasingly playing a crucial role applied in the field
of medical and biological operations. The initiator of Microarray technology is M. Schena et
al. [1] and from past few years microarrays have begun to be used in many fields such as
biomedicine, mostly on cancer and Diabetic, and medical diagnoses. A Deoxyribonucleic
Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface,
such as glass, plastic or silicon chip forming an array. Processing of DNA microarray image
analysis includes three tasks: gridding, segmentation and intensity extraction and at the
stage of processing, the irregularities of shape and spot position which leads to generate
significant errors. This article presents a new spot edge detection method using Window
based Bi-dimensional Empirical Mode Decomposition. On separating spots form the
background area and to decreases the probability of errors and gives more accurate
information about the states of spots we are proposing a spot edge detection via WBEMD.
By using this method we can identify the spots with low density, which leads to increasing
the performance of cDNA microarray images.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
The growing trend of online image sharing and downloads today mandate the need for better encoding and
decoding scheme. This paper looks into this issue of image coding. Multiple Description Coding is an
encoding and decoding scheme that is specially designed in providing more error resilience for data
transmission. The main issue of Multiple Description Coding is the lossy transmission channels. This work
attempts to address the issue of re-constructing high quality image with the use of just one descriptor
rather than the conventional descriptor. This work compare the use of Type I quantizer and Type II
quantizer. We propose and compare 4 coders by examining the quality of re-constructed images. The 4
coders are namely JPEG HH (Horizontal Pixel Interleaving with Huffman Coding) model, JPEG HA
(Horizontal Pixel Interleaving with Arithmetic Encoding) model, JPEG VH (Vertical Pixel Interleaving
with Huffman Encoding) model, and JPEG VA (Vertical Pixel Interleaving with Arithmetic Encoding)
model. The findings suggest that the use of horizontal and vertical pixel interleavings do not affect the
results much. Whereas the choice of quantizer greatly affect its performance.
Mimo radar detection in compound gaussian clutter using orthogonal discrete f...ijma
This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for
Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed
orthogonal waveforms are designed considering the position and angle of the transmitting antenna when
viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized
Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better
detection performance in spikier Clutter.
Design and Development of an Image Based Plant Identification System Using Leafijma
Because of huge diversity of plants existing on earth and large inter-species similarity, the manual process
of identification of plants is difficult and at times, the results generated may be confusing. So, it is
necessary to automate the process of identification of plants to generate faster and accurate results. Among
various parts of plant, leaf is easily available in all seasons and can be easily scanned for understanding
the shape details. So, in this paper, we have worked on plant identification using leaf image. In the process
of identification of plants, the key challenge identified is to keep the size of feature vector reasonable and
still achieve accurate and effective results. As the results are to be interpreted by the user, it was necessary
to understand the theories behind human perception to reduce the semantic gap. We have designed and
developed a tool ‘SbLRS: Shape based Leaf Recognition System’ which is a three-staged system that
performs identification of plant using leaf shape. Our system can be helpful for following users- common
layman person, as replacement for biologists in remote locations, as teaching aid for teachers and
students, for a farmer to identify the plant species and to assist biologists in identification process. The
parameters important for identifying leaf image according to human perception are identified and defined
at three different levels. On the basis of results generated, the effectiveness of SbLRS is compared with
existing contour based methods in terms of recall, precision. It was observed that our tool SbLRS showed
satisfactory results for identification of plant using leaf at small feature vector size and simpler
computations.
This study is a part of design of an audio system for in-house object detection system for visually impaired,
low vision personnel by birth or by an accident or due to old age. The input of the system will be scene and
output as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc) and
also during difficulties. The study proposed techniques to provide speedy detection of objects based on
shapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From a
scene, clusters of objects are formed based on the scale and shape. Searching is performed among the
clusters initially based on the shape, scale, mean cluster value and index of object(s). The minimum
operation to detect the possible shape of the object is performed. In case the object does not have a likely
matching shape, scale etc, then the several operations required for an object detection will not perform;
instead, it will declared as a new object. In such way, this study finds a speedy way of detecting objects.
EXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONSijma
Nowadays, image alteration in the mainstream media has become common. The degree of manipulation is
facilitated by image editing software. In the past two decades the number indicating manipulation of
images rapidly grows. Hence, there are many outstanding images which have no provenance information
or certainty of authenticity. Therefore, constructing a scientific and automatic way for evaluating image
authenticity is an important task, which is the aim of this paper. In spite of having outstanding
performance, all the image forensics schemes developed so far have not provided verifiable information
about source of tampering. This paper aims to propose a different kind of scheme, by exploiting a group of
similar images, to verify the source of tampering. First, we define our definition with regard to tampered
image. The distinctive features are obtained by exploiting Scale- Invariant Feature Transform (SIFT)
technique. We then proposed clustering technique to identify the tampered region based on distinctive
keypoints. In contrast to k-means algorithm, our technique does not require the initialization of k value. The
experimental results over and beyond the dataset indicate the efficacy of our proposed scheme
Leader Follower Formation Control of Ground Vehicles Using Dynamic Pixel Coun...ijma
This paper deals with leader-follower formations of non-holonomic mobile robots, introducing a formation
control strategy based on pixel counts using a commercial grade electro optics camera. Localization of the
leader for motions along line of sight as well as the obliquely inclined directions are considered based on
pixel variation of the images by referencing to two arbitrarily designated positions in the image frames.
Based on an established relationship between the displacement of the camera movement along the viewing
direction and the difference in pixel counts between reference points in the images, the range and the angle
estimate between the follower camera and the leader is calculated. The Inverse Perspective Transform is
used to account for non linear relationship between the height of vehicle in a forward facing image and its
distance from the camera. The formulation is validated with experiments.
Development of a web based social networking system for self-management of di...ijma
This paper presents the design and development of a web-based social networking system for selfmanagement
of diabetes mellitus. The objectives of this development are twofold. First is to enable diabetic
patients to record and monitor their blood glucose levels by using short message service (SMS) or through
a website. Second is to provide social networking functionalities for diabetic patients, healthcare workers,
and other related parties to form online communities for information sharing, support, and collaboration.
With responsive design, the website aims to provide the best possible user experience across devices from
desktops and notebooks to tablets and smart phones.
2da version del 1er trabajo publicado.hace referencia a una clase de presentacion de la plastica,y a 2 formas de evaluacion, la 1ra de manera visual y la 2dq de manera tecnica
Image Deblurring using L0 Sparse and Directional FiltersCSCJournals
Blind deconvolution refers to the process of recovering the original image from the blurred image when the blur kernel is unknown. This is an ill-posed problem which requires regularization to solve. The naive MAP approach for solving the blind deconvolution problem was found to favour no-blur solution which in turn led to its failure. It is noted that the success of the further developed successful MAP based deblurring methods is due to the intermediate steps in between, which produces an image containing only salient image structures. This intermediate image is essentially called the unnatural representation of the image. L0 sparse expression can be used as the regularization term to effectively develop an efficient optimization method that generates unnatural representation of an image for kernel estimation. Further, the standard deblurring methods are affected by the presence of image noise. A directional filter incorporated as an initial step to the deblurring process makes the method efficient to be used for blurry as well as noisy images. Directional filtering along with L0 sparse regularization gives a good kernel estimate in spite of the image being noisy. In the final image restoration step, a method to give a better result with lesser artifacts is incorporated. Experimental results show that the proposed method recovers a good quality image from a blurry and noisy image.
Fast nas rif algorithm using iterative conjugate gradient methodsipij
Many improvements on image enhancemen have been achieved by The Non-negativity And Support
constraints Recursive Inverse Filtering (NAS-RIF) algorithm. The Deterministic constraints such as non
negativity, known finite support, and existence of blur invariant edges are given for the true image. NASRIF
algorithms iterative and simultaneously estimate the pixels of the true image and the Point Spread
Function (PSF) based on conjugate gradients method. NAS-RIF algorithm doesn’t assume parametric
models for either the image or the blur, so we update the parameters of conjugate gradient method and the
objective function for improving the minimization of the cost function and the time for execution. We
propose a different version of linear and nonlinear conjugate gradient methods to obtain the better results
of image restoration with high PSNR.
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.
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.
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge
detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in
second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge
detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained
with back propagation technique using few training patterns but the most important and difficult part is to
identify the correct and proper training set.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
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.
AN ADAPTIVE THRESHOLD SEGMENTATION FOR DETECTION OF NUCLEI IN CERVICAL CELLS ...cscpconf
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image analysis could swap manual interpretation. This paper proposes a method for the detection of cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on the size of the segmented nucleus which therefore helps in differentiating abnormality among the cells.
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
A Flexible Scheme for Transmission Line Fault Identification Using Image Proc...IJEEE
This paper describes a methodology that aims to find and diagnosing faults in transmission lines exploitation image process technique. The image processing techniques have been widely used to solve problem in process of all areas. In this paper, the methodology conjointly uses a digital image process Wavelet Shrinkage function to fault identification and diagnosis. In other words, the purpose is to extract the faulty image from the source with the separation and the co-ordinates of the transmission lines. The segmentation objective is the image division its set of parts and objects, which distinguishes it among others in the scene, are the key to have an improved result in identification of faults.The experimental results indicate that the proposed method provides promising results and is advantageous both in terms of PSNR and in visual quality.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Image deblurring based on spectral measures of whiteness
1. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.2, April 2014
DOI : 10.5121/ijma.2014.6205 51
IMAGE DEBLURRING BASED ON SPECTRAL
MEASURES OF WHITENESS
Swathi and Bhoopathy Bagan
Department of Electronics Engineering, Madras Institute of Technology,
Anna University, Chennai, India
ABSTRACT
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
KEYWORDS
Image deconvolution, blind image deblurring, Image restoration, whiteness measures
1. INTRODUCTION
Image Deblurring is one of the most important problems in image restoration. In this process, the
observed image is modeled as the degradation of the sharp image, by convolution with blurring
filter, plus some noise. Image deconvolution or deblurring has been widely used in the
enhancement of images in photos and video cameras, in astronomy, in tomography, in remote
sensing and in other biomedical imaging techniques.
Image Deblurring can be divided into two different types namely non-blind image deblurring and
blind image deblurring. In non-blind image deblurring, the blurring filter is assumed to be known
whereas in blind image deblurring both the image and the blurring operator are partially or totally
unknown.
Due to the ill-conditioned nature of the blurring filter, the deblurring process is very difficult in
non-blind image deblurring though the blurring operator is known.
2. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.2, April 2014
52
Blind image deblurring is much more challenging when compared to non-blind image deblurring.
Here the deblurring process leads to infinite number of solutions due to the presence of many
pairs of blurring filters and image estimates. To solve this problem, most blind image deblurring
methods restrict the type of blur filter based on either the use of regularizers [2], or through the
use of parametric models [1], that are either through the soft way or hard way. This method aims
to estimate the unknown blur from the observed blurred image and hence recover the original
sharp image.
A recent iterative blind image deblurring method [7], uses a large regularization weight, which
estimates the main features of the image. By decreasing the regularization parameter gradually,
this method learns the image details. It requires manual stopping which is the main drawback.
The methods developed for estimating the regularization parameter of non-blind image
deblurring cannot be used for the blind case.
The already existing blind image deblurring methods require some tuning of the regularizing
parameters. More work has been devoted to choose the accurate regularization parameter. The
Discrepancy Principle (DP) [3] chooses the regularization parameter so that the variance of the
residual matches that of the noise. Here the residual is the difference between the observed image
and the blurred estimate. An extension of DP uses not only variance, but also the residual
moments [4]. The method for automatically adjusting the regularization parameter for non-blind
image deblurring is not the same for blind image deblurring. Stein’s unbiased risk estimate
(SURE) [5], [8], provides an estimate of the mean squared error (MSE), by assuming knowledge
of the noise distribution and requiring an accurate estimate of its variance [6]. SURE-based
approaches assume full knowledge of the degradation model and thus are not suitable for blind
image deblurring. In blind image deblurring most of the existing methods require some tuning to
obtain the regularization parameter.
In this paper, the iterative blind image deblurring method is based on the assumption that the
additive noise is spectrally white. The residual associated with the deblurred image becomes
white, if the blurring filter is correctly estimated, whereas with a wrong filter, the image estimate
will exhibit structured artifacts which are not white. This method takes into account the prior
information on the blurring filter. The estimation is guided to a good solution by first
concentrating on the main edges of the image, and progressively dealing with smaller details. The
proposed criterion yields significant results.
2. BLIND IMAGE DEBLURRING METHOD
Blind image deblurring method is used to deblur the degraded image, without prior knowledge of
the blur kernel and additive noise.
The image degradation can be modeled as
where y is the degraded image, s is the sharp unknown image, b is the blur kernel and n is the
noise involved. The deblurred method finds the cost function with respect to the sharp image s
and the blur kernel b and is given by
3. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.2, April 2014
53
here the first term i.e., the data fidelity term results from assuming that the noise is white and
Gaussian, α is the regularization parameter and Ф(s) is the regularizing function which contains
prior information about s. Many image deblurring methods minimize the cost function (2)
iteratively.
The step involved in blind image deblurring process is shown in Figure 1. The first step involved
is to set the blur kernel b to an identity operator and also to initialize the degraded image as the
image estimate. In this deblurring process, the iteration starts with a high regularization value and
is gradually decreased by multiplying it with a constant β which is less than 1. Too large values
of α lead to cartoon-like images or over smoothed images while too small values leads to images
influenced by more noise. Based on this, the new image estimate and blur estimate are
formed. The stopping criterion of this blind deblurring method corresponds to setting the final
value of the regularizing parameter which is based on the whiteness measures.
The cost function defined in (2) depends on the data fidelity term, the regularization value and the
regularizing function Ф(s). The regularizer Ф(s) was chosen as it favors the sharp edges and also
for certain values of the parameters, the prior is close to the actual observed edges. The sparse
edges are quite visible in the first few iterations, but are almost unnoticeable in the final iteration.
When the image to be deblurred is noisy, to prevent the appearance of the noise in the deblurred
image, the final regularization cannot be made so weak.
Figure 1. Process Involved in Blind Image Deblurring
4. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.2, April 2014
54
The proposed method consists in selecting the regularization value or the final iteration of the
algorithm that maximize the whiteness measure. This measure exhibits a clear peak and the
method is stopped as soon as the whiteness measure starts to decrease.
2.1. Edge Detection
At the starting of the deblurring process, the estimate of the blur operator is poor and a strong
regularization is required to make the edges sharper in the estimated image. During early
iterations, only the main edge of the estimated image is identified. This is because of the high
regularization value. The surviving sharp edges allow the estimate of the blurring operator to be
improved. In the following iterations, the fainter edges are determined with the help of somewhat
lower value of α and the improved filter. As the iteration increases, the smaller features are
predicted with a gradual decrease of α.
The edge detection filters in different orientations (Figure 2) are used for this prediction.
Figure 2. Edge Detection filters in the four orientations
Thus the sharp edges of the image estimate when compared with the blurred image, allows to
learn and improve the estimate of the blurring filter and also to learn the finer image details.
2.2. Whiteness Measures
The selection of the stopping criteria are based on the measures of the fitness of the image
estimate and the blur estimate. This is analysed based on the residual image given by
The nature of the residual is then compared with the additive noise n. As this noise is assumed to
be uncorrelated and spectrally white, the whiteness measure is used to access the adequacy of the
image and the blur estimates. The residual image (3) is normalized to zero mean and unit
variance. The auto-correlation of the normalized residual denoted by r is given by
where the sum extends over the whole residual image.
The measure of whiteness of the residual image (3) is given as the distance between the auto-
correlation (4) and the delta function. The delta function of the spectrally white image at the
5. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.2, April 2014
55
origin is given by δ (p,q) = 1, if p = q = 0, Otherwise δ (p,q) = 0. The delta function is the auto-
covariance of the spectrally white image.
The whiteness measure is the energy of auto-correlation beyond the origin given by
Here the minus sign is used to make MR larger for whiter residuals and .
3. EXPERIMENTS AND RESULTS
The benchmark images such as Lena, Barbara, and Cameraman are considered in this experiment.
All images are of size 256 x 256 pixels. Blurs like out-of-focus and uniform blur are used; all are
of size 9 x 9 (i.e. 2l + 1 x 2l + 1). Different blurred signal-to-noise-ratio (BSNR) like 30dB,
40dB, 50dB is used.
For evaluating the quality of the results, estimation of increase in signal to noise ratio (ISNR) is
done. The residual image exhibit structures that are far from being spectrally white, for early
iterations and also later iterations. For noisy blurred images, the estimated image quickly
becomes degraded if the stopping point is chosen after the optimal point. For non-noisy blurred
images, the choice of the stopping point has no influence after that point. At the best ISNR, the
residual image has little structure, thus being approximately white and the auto-covariance is
essentially a delta function. When the whiteness measure MR(r) starts decreasing, the stopping
criterion of the proposed method gets satisfied.
The test image (Fig. 3a) is blurred using a uniform blur filter with 30dB BSNR (Fig. 3b). The
image estimate for the blurred image at different iterations and their respective blur estimates are
shown in the figure 3. The final deblurred image (Fig. 3g) and its respective blur estimate (Fig.
3h) obtained show the effectiveness of the proposed method.
The ISNR values attained in the tests can be considered good, taking into account that the method
under test is blind. The automatic stopping criteria show results that are slightly worse (-0.38 dB)
than the best ISNR obtained by manual stopping, showing that the automatic criterion tends to
stop earlier. Two filters namely uniform and out-of-focus blur for three different BSNR values
are taken.
The ISNR values obtained are just slightly less than the ISNR values obtained by manual
stopping in the proposed approach.
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Figure. 3. Barbara image with uniform blur. (a) Sharp image (b) Image blurred with 30dB noise
(c)-(h) Images and the respective blur estimates in the iterative process
Table 1. ISNR values obtained for test images using different filters
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Table 1 shows the increase in signal-to-noise ratio (ISNR) obtained by the proposed criterion for
the different test images considered.
4. CONCLUSIONS
A new criterion has been proposed to select the regularization parameter and to stop iterative
blind image deblurring method. Good estimates of both the image and the blurring operator are
reached by initially considering the main edges of the image and then progressively handling the
smaller ones. The method is based on the whiteness measures of the residual image. This
approach does not require any knowledge about the convolution operator. Experimental tests on a
variety of images degraded with out-of-focus and uniform blurs show good results. The ISNR
values obtained from the automatic stopping criterion method are somewhat similar (0.38dB less)
to the values obtained by manual stopping.
ACKNOWLEDGEMENTS
I would like to thank my guide Dr.K.Bhoopathy Bagan for his guidance, constant encouragement
and support. His extensive vision and creative thinking has been a source of inspiration
throughout this project. And I also thank my review committee members for their valuable
suggestions on improvement of this work.
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