Due to narrow-band noise signals in transmission channels, visible lines of disturbance can appear in video images. In this paper, an adaptive method based on two-level filtering is proposed to enhance the visual quality of such images. In the first level, an adaptive orientation selective filter detects and clears the noisy lines in the image. In the second level, a median filter repairs defects resulting from the orientation selective filtering process and also filters the wide-band impulsive noise. It was observed that in case of periodic noisy lines in TV images, this filtering technique can sufficiently enhance the image quality and improve the SNR level.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image denoising using new adaptive based median filtersipij
Noise is a major issue while transferring images through all kinds of electronic communication. One of the
most common noise in electronic communication is an impulse noise which is caused by unstable voltage.
In this paper, the comparison of known image denoising techniques is discussed and a new technique using
the decision based approach has been used for the removal of impulse noise. All these methods can
primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at
first introduced and then analysed with various simulation results using MATLAB. Most of the previously
known techniques are applicable for the denoising of images corrupted with less noise density. Here a new
decision based technique has been presented which shows better performances than those already being
used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square
error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images..
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 International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
An Application of Second Generation Wavelets for Image Denoising using Dual T...IDES Editor
The lifting scheme of the discrete wavelet transform
(DWT) is now quite well established as an efficient technique
for image denoising. The lifting scheme factorization of
biorthogonal filter banks is carried out with a linear-adaptive,
delay free and faster decomposition arithmetic. This adaptive
factorization is aimed to achieve a well transparent, more
generalized, complexity free fast decomposition process in
addition to preserve the features that an ordinary wavelet
decomposition process offers. This work is targeted to get
considerable reduction in computational complexity and power
required for decomposition. The hard striking demerits of
DWT structure viz., shift sensitivity and poor directionality
had already been proven to be washed out with an emergence
of dual tree complex wavelet (DT-CWT) structure. The well
versed features of DT-CWT and robust lifting scheme are
suitably combined to achieve an image denoising with prolific
rise in computational speed and directionality, also with a
desirable drop in computation time, power and complexity of
algorithm compared to all other techniques.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image denoising using new adaptive based median filtersipij
Noise is a major issue while transferring images through all kinds of electronic communication. One of the
most common noise in electronic communication is an impulse noise which is caused by unstable voltage.
In this paper, the comparison of known image denoising techniques is discussed and a new technique using
the decision based approach has been used for the removal of impulse noise. All these methods can
primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at
first introduced and then analysed with various simulation results using MATLAB. Most of the previously
known techniques are applicable for the denoising of images corrupted with less noise density. Here a new
decision based technique has been presented which shows better performances than those already being
used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square
error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images..
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 International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
An Application of Second Generation Wavelets for Image Denoising using Dual T...IDES Editor
The lifting scheme of the discrete wavelet transform
(DWT) is now quite well established as an efficient technique
for image denoising. The lifting scheme factorization of
biorthogonal filter banks is carried out with a linear-adaptive,
delay free and faster decomposition arithmetic. This adaptive
factorization is aimed to achieve a well transparent, more
generalized, complexity free fast decomposition process in
addition to preserve the features that an ordinary wavelet
decomposition process offers. This work is targeted to get
considerable reduction in computational complexity and power
required for decomposition. The hard striking demerits of
DWT structure viz., shift sensitivity and poor directionality
had already been proven to be washed out with an emergence
of dual tree complex wavelet (DT-CWT) structure. The well
versed features of DT-CWT and robust lifting scheme are
suitably combined to achieve an image denoising with prolific
rise in computational speed and directionality, also with a
desirable drop in computation time, power and complexity of
algorithm compared to all other techniques.
Performance Assessment of Several Filters for Removing Salt and Pepper Noise,...IJEACS
Digital images are prone to a variety of noises. De-noising of image is a crucial fragment of image reconstruction procedure. Noise gets familiarized in the course of reception and transmission, acquisition and storage & recovery processes. Hence de-noising an image becomes a fundamental task for correcting defects produced during these processes. A complete examination of the various noises which corrupt an image is included in this paper. Elimination of noises is done using various filters. To attain noteworthy results various filters have been anticipated to eliminate these noises from Images and finally which filter is most suitable to remove a particular noise is seen using various measurement parameters.
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
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.
This research focus on image sharpness and quality
using a self-organizing migration algorithm (SOMA) with
curvelet based nonlocal means (CNLM) denoising is presented.
In this paper, first transform curvelet is using on the noisy image
obtain image. Find the comparison of 2 pixels in the noisy picture
which is evaluated depend on these curvelet produced pictures
which include complementary picture capabilities at particularly
excessive noise levels and the noisy picture at especially low noise
levels. Then pixel comparison and noisy photograph are used to
denoised end outcome found applying NLM technique. SOMA
obtains better quality with the aid of varying threshold on the
basis of image pixels. The threshold can be determined using
lower and upper value of noisy image. Quantitative evaluations
illustrate that the proposed scheme perform more enhanced than
the other filters namely median filter (MF) progressive switching
median filter (PSMF), NLM, CNLM denoising process in
conditions of noise removal and detail protection. Using different
parameters for example Peak Signal Noise Ratio (PSNR), means
Structural Similarity Matrix (MSSIM) and SSIM for noise free
image. It is illustrated that the improved scheme provides an
excessive degree of noise removal whilst maintaining the edges
and other information in the image. In this study, algorithm is
tested on dissimilar kind of noise explicitly, Random Valued
Impulse Noise (RVIN), Gaussian Noise and Salt and Pepper
(SNP) Noise with varying noise density from 10 to 90%. The
proposed system proves better performance on high noise
density.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
Analysis of Various Image De-Noising Techniques: A Perspective Viewijtsrd
A critical issue in the image restoration is the problem of de noising images while keeping the integrity of relevant image information. A large number of image de noising techniques are proposed to remove noise. Mainly these techniques are depends upon the type of noise present in images. So image de noising still remains an important challenge for researchers because de noising techniques remove noise from images but also introduces some artifacts and cause blurring. In this paper we discuss about various image de noising and their features. Some of these techniques provide satisfactory results in noise removal and also preserving edges with fine details present in images. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise which is modeled as a Gaussian. Speckle noise is observed in ultrasound images whereas Rician noise affects MRI images. The scope of the paper is to focus on noise removal techniques for natural images. Bhavna Kubde | Prof. Seema Shukla "Analysis of Various Image De-Noising Techniques: A Perspective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29629.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29629/analysis-of-various-image-de-noising-techniques-a-perspective-view/bhavna-kubde
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
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.
Noise Reduction in Magnetic Resonance Images using Wave Atom ShrinkageCSCJournals
De-noising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution. Unlike additive Gaussian noise, Rician noise is signal dependent, and separating signal from noise is a difficult task. An efficient method for enhancement of noisy magnetic resonance image using wave atom shrinkage is proposed. The reconstructed MRI data have high Signal to Noise Ratio (SNR) compared to the curvelet and wavelet domain de-noising approaches.
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
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
Performance Assessment of Several Filters for Removing Salt and Pepper Noise,...IJEACS
Digital images are prone to a variety of noises. De-noising of image is a crucial fragment of image reconstruction procedure. Noise gets familiarized in the course of reception and transmission, acquisition and storage & recovery processes. Hence de-noising an image becomes a fundamental task for correcting defects produced during these processes. A complete examination of the various noises which corrupt an image is included in this paper. Elimination of noises is done using various filters. To attain noteworthy results various filters have been anticipated to eliminate these noises from Images and finally which filter is most suitable to remove a particular noise is seen using various measurement parameters.
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
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.
This research focus on image sharpness and quality
using a self-organizing migration algorithm (SOMA) with
curvelet based nonlocal means (CNLM) denoising is presented.
In this paper, first transform curvelet is using on the noisy image
obtain image. Find the comparison of 2 pixels in the noisy picture
which is evaluated depend on these curvelet produced pictures
which include complementary picture capabilities at particularly
excessive noise levels and the noisy picture at especially low noise
levels. Then pixel comparison and noisy photograph are used to
denoised end outcome found applying NLM technique. SOMA
obtains better quality with the aid of varying threshold on the
basis of image pixels. The threshold can be determined using
lower and upper value of noisy image. Quantitative evaluations
illustrate that the proposed scheme perform more enhanced than
the other filters namely median filter (MF) progressive switching
median filter (PSMF), NLM, CNLM denoising process in
conditions of noise removal and detail protection. Using different
parameters for example Peak Signal Noise Ratio (PSNR), means
Structural Similarity Matrix (MSSIM) and SSIM for noise free
image. It is illustrated that the improved scheme provides an
excessive degree of noise removal whilst maintaining the edges
and other information in the image. In this study, algorithm is
tested on dissimilar kind of noise explicitly, Random Valued
Impulse Noise (RVIN), Gaussian Noise and Salt and Pepper
(SNP) Noise with varying noise density from 10 to 90%. The
proposed system proves better performance on high noise
density.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
Analysis of Various Image De-Noising Techniques: A Perspective Viewijtsrd
A critical issue in the image restoration is the problem of de noising images while keeping the integrity of relevant image information. A large number of image de noising techniques are proposed to remove noise. Mainly these techniques are depends upon the type of noise present in images. So image de noising still remains an important challenge for researchers because de noising techniques remove noise from images but also introduces some artifacts and cause blurring. In this paper we discuss about various image de noising and their features. Some of these techniques provide satisfactory results in noise removal and also preserving edges with fine details present in images. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise which is modeled as a Gaussian. Speckle noise is observed in ultrasound images whereas Rician noise affects MRI images. The scope of the paper is to focus on noise removal techniques for natural images. Bhavna Kubde | Prof. Seema Shukla "Analysis of Various Image De-Noising Techniques: A Perspective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29629.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29629/analysis-of-various-image-de-noising-techniques-a-perspective-view/bhavna-kubde
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
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.
Noise Reduction in Magnetic Resonance Images using Wave Atom ShrinkageCSCJournals
De-noising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution. Unlike additive Gaussian noise, Rician noise is signal dependent, and separating signal from noise is a difficult task. An efficient method for enhancement of noisy magnetic resonance image using wave atom shrinkage is proposed. The reconstructed MRI data have high Signal to Noise Ratio (SNR) compared to the curvelet and wavelet domain de-noising approaches.
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
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
Finite Element Investigation of Hybrid and Conventional Knee ImplantsCSCJournals
Total Knee arthroplasty (TKA) procedures relieve arthritic pain and restore joint function by replacing the contact surfaces of the knee joint. These procedures are often performed following arthritic degeneration of the joint causing the patient pain. Cobalt-chrome, stainless steel (316L grade) and titanium alloys are widely used in the majority of distal femoral implants in TKA procedures. The use of such stiff materials causes stress shielding (i.e. a lack of mechanical stresses being experienced by the bone surrounding the implant) leading to gradual bone loss and implant failure. The aim of this paper is to develop a new hybrid knee implant which combines a polymer-composite (CF/PA-12) with an existing commercial implant system (P.F.C.® Sigma™) made from stainless steel. This hybrid implant is expected to alleviate stress shielding and bone loss by transferring much more load to the femur compared to conventional metallic implants. Results of the FEA simulations showed that the CF/PA-12 lined femoral component generated almost 63% less in peak stress compared to the regular stainless steel component, indicating more load transfer to the bone and consequently alleviating bone resorption.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
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journal of engineering, online Submission
Accelerated Joint Image Despeckling Algorithm in the Wavelet and Spatial DomainsCSCJournals
Noise is one of the most widespread problems present in nearly all imaging applications. In spite of the sophistication of the recently proposed methods, most denoising algorithms have not yet attained a desirable level of applicability. This paper proposes a two-stage algorithm for speckle noise reduction jointly in the wavelet and spatial domains. At the first stage, the optimal parameter value of the spatial speckle reduction filter is estimated, based on edge pixel statistics and noise variance. Then the optimized filter is used at the second stage to additionally smooth the approximation image of the wavelet sub-band. A complexity reduction algorithm for wavelet decomposition is also proposed. The obtained results are highly encouraging in terms of image quality which paves the way towards the reinforcement of the proposed algorithm for the performance enhancement of the Block Matching and 3D Filtering algorithm tackling multiplicative speckle noise.
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.
Removal of noise is a determining track in
the image rebuilding process, but denoising of image remains a
claiming problem in upcoming analysis accomplice along
image processing. Denoising is utilized to expel the noise from
corrupted image, where as we need to maintain the edges and
other detailed characteristics almost accessible. This noise gets
imported during accretion, transmitting & receiving and
storage & retrieval techniques. In this paper, to discover out
denoised image the modified denoising technique and the local
adaptive wavelet image denoising technique can be obtained.
The input (noisy image) is denoised with the help of modified
denoising technique which is form on wavelet domain as well as
spatial domain along with the local adaptive wavelet image
denoising technique which is form on wavelet domain. In this
paper, I have appraised and analyzed achievements of
modified denoising technique and the local adaptive wavelet
image denoising technique. The above procedures are
contemplated with other based on PSNR between input image
and noisy image and SNR between input image and denoised
image. Simulation and experimental outgrowth for an image
reflects as the mean square error of the local adaptive wavelet
image denoising procedure is less efficient as compare to
modified denoising procedure including the signal to noise
ratio of the local adaptive wavelet image denoising technique is
effective than other approach. Therefore, the image after
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An Adaptive Two-level Filtering Technique for Noise Lines in Video Images
1. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 270
An Adaptive Two-level Filtering Technique for Noise Lines in
Video Images
Baris Baykant ALAGOZ baykant.alagoz@inonu.edu.tr
Department of Electrical and Electronics Engineering,
Inonu University, Malatya, Turkey
Mehmet Emin TAGLUK mehmet.tagluk@inonu.edu.tr
Department of Electrical and Electronics Engineering,
Inonu University, Malatya, Turkey
Abstract
Due to narrow-band noise signals in transmission channels, visible lines of disturbance can
appear in video images. In this paper, an adaptive method based on two-level filtering is
proposed to enhance the visual quality of such images. In the first level, an adaptive orientation
selective filter detects and clears the noisy lines in the image. In the second level, a median filter
repairs defects resulting from the orientation selective filtering process and also filters the wide-
band impulsive noise. It was observed that in case of periodic noisy lines in TV images, this
filtering technique can sufficiently enhance the image quality and improve the SNR level.
Keywords: Adaptive noise filter, Wireless video image enhancement.
1. INTRODUCTION
In long distance wireless video transmission systems, periodic noise line patterns commonly
appear on the received image. Such noise line patterns in wireless transmission are mostly
caused from long-term narrow-band signal interference to the communication channel. In many
cases, such interference of by noise signals in the channel is unavoidable and therefore the
removal of these noise signals has to be carried out on the received image by using filtering
techniques. A basic notch reject filter has been applied for the removal of the noise lines on the
images received from the long-distance space missions [1]. Nowadays, with increasing usage of
wireless video transmission systems in day-to-day applications there is an increasing demand to
develop such filtering techniques to work on received images.
In practice, unlicensed transmitter interference, noise in electronics, multi-path effects, loss of
horizontal or vertical synchronization are all seen to cause periodic noise lines on the received
image. These noise lines can severely mislead computer vision algorithms employed in
autonomous remote control systems used in unmanned vehicles [2-5].
Periodic noise lines in images are commonly seen in imaging systems, which use a row scanning
mechanism in the construction of the image data when a long-term noise signal affects the
system. For example, mechanical and acoustic vibrations in force sensors were seen to decrease
the signal to noise ratio (SNR) of images scanned by an Atomic Force Microscope (AFM) at video
rate [6].
In the image enhancement field, a variety of methods have been developed to filter out the effects
of random noise on images. Most of these studies have been focused on preserving singular
features of the image such as edges, while smoothing other segments of the image [7-11].
Alternatively a nonlinear adaptive filter based on a neural network has been proposed for
reducing the additive noise [12]. Many adaptive image restoration methods that analyse the noise
and optimise the behaviour of the filter to improve overall filtering performance [13-15].
Specifically, for digital TVs the filters compromise edge detection, and an automated modification
of filter coefficients has been addressed in detail by Chan et al. [7, 10]. These filters were mainly
2. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 271
developed to deal with random additive noise in images. In their mathematical formulation, the
received signal has been defined as:
)()()( xnxuxur += , (1)
where, )(xn is the random noise and )(xu is the original noise-free image. Due to the fact that
the noise components are treated as a part of the image signal )(xu , such filters designed for
removing random noise are not able to effectively deal with narrow-band noise lines.
In our study, we assume that the received signal has a narrow-band noise signal besides
a random noise signal. Under this assumption, a noise signal can be modelled as:
)()()()( xnxnxuxu nr ++= (2)
where, )(xnn represents the narrow-band noise.
To enhance the received image coming from highly noisy channel modelled by equation (2), we
proposed a filtering structure which is composed of a frequency domain orientation selective
notch reject filter for the elimination of the narrow-band noise ( )(xnn ) [1, 16] and a spatial
median filter for impulsive noise [8]. The block diagram of such a two-level filtering is presented in
Figure 1.
FIGURE 1: Block diagram of two-level filtering.
A noisy image is first transformed to red, green and blue color channels, each color channels
being solely filtered by the two-level filter designed. In order to adapt the angle of notch reject
band to the slope of the noise lines in the received images, the proposed orientation selective
filter first detects the spectral region belonging to noise lines, via measuring the local spectral
power density, and then it orients the reject band onto the region where the power of noise lines
is intensified.
2. PROPOSED METHOD
Frequency domain filtering [16] usually provides a good performance in the filtering of narrow-
band noise signals, because of allow us eliminating frequency components in a narrow frequency
band. Basic design methodology of a frequency domain filter is as follows: The spatial two-
dimensional image data are first transformed into frequency components via a two-dimensional
Fast Fourier Transform (2DFFT) and then a two-dimensional mask is applied to suppress
undesired frequency components, and finally an inverse Fast Fourier Transform (2DIFFT) is used
to obtain the filtered image in the spatial domain, as illustrated in Figure 2.
Frequency
Domain
Filter
u(x)+nn(x)+n(x)
Two-level filtering system
Spatial
Filter
u(x)+n(x) uo(x)
3. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 272
FIGURE 2. Frequency domain filtering with component masking
Masking is done by multiplication of each mask element with elements of R , G and B image
as follows:
),(),(),( vuMkvuRvuR ffm ⋅= ,
),(),(),( vuMkvuGvuG ffm ⋅= , (3)
),(),(),( vuMkvuBvuB ffm ⋅= ,
where, fR , fG and fB are the FFT of the R , G and B matrix, fmR and , fmG and fmB
are the masked frequency components of the RGB image. The mask matrix Mk has real value,
RvuMk ∈),( , in such cases this mask takes effect on the amplitude of the spectral components
of images. The phases of frequency components are preserved in this masking operation.
In this study, we introduce a mask φMk generation function that provides a directional stop-
band over high frequencies to suppress periodic line patterns in the image for a given φ angle.
The proposed mask is formed from superposition a suppressing channel passing through the
origin of the 2D spectrum and a directional pass band at the origin.
The construction of the mask is as follows. The suppressing channel, whose direction is
controlled by a given angle, φ , was constructed on a base line passing through the origin
)
2
,
2
(
NM
as presented in Figure 3.
2
)
2
()tan(
MN
vu +−⋅−= φ , (4)
where M and N determine the size of the mask, φMk , and φ is angle of the base line. The
shortest distance of any point ),( vu to this base line, denoted by ),,( φvuds , was derived as:
2-Dimensional
Fast Fourier
Transform
(FFT)Digital
Đmage
Component
Masking
Frequency Domain Filtering
Filtered
Đmage
2-Dimensional
Inverse Fast
Fourier
Transform
(IFFT)
4. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 273
=−
=−
<<−+−
=
0
0
0022
270,
2
90,
2
27090,)),,(()),,((
),,(
φ
φ
φφφ
φ
M
u
N
v
vuvvvuuu
vud
bb
s , (5)
where )),,(),,,(( φφ vuvvuu bb is the nearest point of the base line to the point ),( vu on the
mask, and calculated by the following expressions:
22
),,()tan(),,(
MN
vuvvuu bb +
−⋅= φφφ . (6)
)1)((tan2
)tan(2)tan(2)(tan
),,( 2
2
+⋅
⋅⋅+⋅−⋅+⋅
=
φ
φφφ
φ
uMvN
vuvb . (7)
The shortest distance of any point ),( vu to the centre of the mask, denoted by ),( vudc , is
calculated as:
22
)
2
()
2
(),(
N
v
M
uvudc −+−= . (8)
The mask generation function is then formed as:
)
),(
exp()
),,(
exp())
),,(
exp(1(),( 2
2
2
2
2
s
s
c
c
s
s vudvudvud
vuMk
αα
φ
α
φ
φ −⋅−+−−= , (9)
or in a more compact form:
)
),,(
exp(1)
),,(
exp(1),( 2
2
2
2
s
s
c
c vudvud
vuMk
α
φ
α
φ
φ −⋅
−−+= , (10)
where sα and cα are the standard deviations determining the width of directional suppression
channel. These parameters also control smoothness of the transition from the stop to pass band
of the filter. This smooth transition consequently eliminates ringing effects appearing around
patterns in the image. In Figure 4, Mk matrix generated for various φ are illustrated.
The angle φ is adaptively determined in a range of [ ]maxmin ,φφ by applying the following steps:
Step 1: Calculate )(φPw for all φ angles from minφ to maxφ . ( )(φPw , expressed by equation
(11), is the average spectral power in the image spectrum that is effected by the suppression
channel of φMk )
Step 2: Use the φMk mask in filtering for the φ , at which )(φPw is a maximum and value of this
maximum exceeds a predefined filter activation threshold ( thP ). In the case, all )(φPw
[ ]maxmin ,φφφ ∈ are lower than thP , the current image can bypass filtering. This implies that the
noise lines do not have enough power to activate filtering.
5. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 274
FIGURE 3. A contour plot of a suppression channel and its relevant parameters with respect to a base line
passing through the centre of the spectrum.
The average image power in the suppression channel )(φPw can be calculated by:
∑∑
∑∑
−
=
−
=
−
=
−
=
++⋅−
−
=
1
0
1
0
222
1
0
1
0
)),(),(),(()),,(1(
)),,(1(
1
)(
M
u
N
v
fffM
u
N
v
vuBvuGvuRvuMk
vuMk
Pw φ
φ
φ (11)
The term )),,(( φvuMk−1 in equation (11) is the weighting function for spectral components.
Equation (11) is a discrete two dimensional extension of a single variable weighted average
spectral power given as:
dffIfw
dffw
Pw ∫
∫
⋅=
2
)()(
)(
1
for a weight function )( fw .
The choice of cα and sα parameters are in fact based on trial and error. However it was
observed that, in this particular application, for TV images with a size of 640x480 pixel, then
2828=cα and 316=sα can give a satisfactory results.
After removing the noise line patterns from the image by adaptive frequency domain filtering, a
3x3 median filter is applied to the image in order to improve the image quality. This median
filtering reduces the deformations resulting from the removal of noise line patterns, as well as the
impulsive noise on the filtered image.
6. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 275
FIGURE 4. (a) Mk matrix generated for
0
90=φ (b) Mk matrix generated for
0
120=φ and (c)
Mk matrix generated for
0
80=φ
3. EXPERIMENTAL RESULTS
In Figure 5 and in Figure 6, TV images from a real media broadcast captured by a commercial TV
Card were enhanced by the adaptive two-level filtering process described in the previous
sections. The calculated spectral power as a function angle φ and the resulting Mk mask are
given in the sub-figures (c) and (d), respectively.
(a)
100 200 300 400
50
100
150
200
250
300
350
400
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Allowed
Suppressed
Φ
ΦminΦmax
(c)(b)
100 200 300 400
100
200
300
400
100 200 300 400
100
200
300
400
7. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 276
FIGURE 5. (a) Noise test image from TV, (b) Enhanced image by filter system ( 2828=cα , 316=sα
) , (c) Generated Mk matrix, and (d) )(φPw values in the adaptation process. The filter applied a mask
for φ =
0
90 .
(a)
(b)
100 200 300 400 500 600
100
200
300
400
500
Mk
(c)
70 80 90 100 110
0
0.5
1
1.5
2
2.5
x 10
10
Φ
Pw(Φ)
Pth
[ Decree ]
(d)
8. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 277
FIGURE 6. (a) Noise test image from TV, (b) Enhanced image by filter system ( 2828=cα , 316=sα
) , (c) Generated Mk matrix, and (d) )(φPw values in the adaptation process. The filter applied a mask
for φ =
0
90 .
(a)
100 200 300 400 500 600
100
200
300
400
500
Mk
(c)
70 80 90 100 110
0
5
10
15
x 10
9
Φ
Pw(Φ) Pth
[ Decree ]
(d)
(b)
9. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 278
FIGURE 7. (a) and (b) are noise test images and their frequency spectrums , (c) and (d) are adaptive
orientation selective filtering results and their frequency spectrums , (e) and (f) are median filtered images of
(c) and their frequency spectrums..
In Figure 7, the effects of filtering at each level on the test image and its frequency spectrum are
illustrated. After cleaning noise line patterns from the image, as seen in Figure 7(c), median
filtering with 3x3 windowing can be seen to effectively restore the image from impulsive noise and
deformation resulting from the frequency filtering. Median value with respect to image data in 3x3
windows have an effect of repairing notch filtering that is a vertical suppression band passing
through the centre in Figure 7(d). This effect can be seen as strengthening some of the frequency
components in the vertical suppression band in Figure 7(f).
4. TEST ON SYNTHETIC IMAGE AND SNR LEVELS
In this section, the noisy television test image shown in Figure 8(a) was distorted by a synthetic
noise line pattern. This additive noise was generated by a sinusoidal signal with an amplitude of
80 and a frequency set to 0.001, as in Figure 8(c), and a pseudo-random noise signal with an
uniform distribution in the interval ( )50,50− , as in Figure 8(d). After adding these noise signals
to the television test image in accordance with Equation 2, a noise test image was obtained as
shown in Figure 8(b).
10. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 279
FIGURE 8. (a) and (b) are noise test images and their frequency spectrums , (c) and (d) are adaptive
orientation selective filtering results and their frequency spectrums , (e) and (f) are median filtered images of
(c) and their frequency spectrums.
The noisy television test image given in Figure 8(b) was enhanced by using three different basic
filters and the proposed two-level filtering, and the results obtained are demonstrated in Figure 9.
In Figure 9 it can be seen that the median-filter and Wiener filter with window size of 3x3 did not
sufficiently remove the line patterns in the test image. Even though the Gaussian low pass filter
blurred the image, the noise lines are still visible. The proposed method detected the noise lines
at
0
83=φ and adapted the orientation selective filter according to the noise lines. After the
application of both orientation selective filter in the frequency domain and the median filter in the
spatial domain the noise lines were removed without too much blurring of the image as seen in
Figure 9(d).
11. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 280
Figure 9. (a).Median filtered image, (b) Wiener filtered image, (c) Low pass filtered image by a Gaussian
function mask, (d) The proposed two-level filtered image.
(d)
Mk
50 100 150 200 250 300
50
100
150
200
70 80 90 100 110
0
2
4
6
8
10
x 10
9
Pth
Φ
Pw(Φ)
[ Decree ]
12. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 281
FIGURE 10. Generated Mk matrix and )(φPw values in the adaptation process. The filter
was found to be best when φ =
0
83 .
In Figure 11, we present Signal to Noise Ratio (SNR) of these filters on the noisy television test
image. In these tests, the thP of the orientation selective filter activation was set to
9
10.3 . In this
case, when the SNR of the received image is over 40 dB, the noise level remains below thP and
the orientation selective filtering was bypassed and the adaptive two level filtering performed the
median filtering on the received image, solely. When the SNR of the test image was decreased to
lower levels, the proposed two-level filtering detected the visible noise line patterns in the
received image and removed them from the image. In this way, it maintains higher SNRs in the
case of a video transmission in highly noisy channel.
FIGURE 11. SNR of the filtered images for various additive noise levels.
5. CONCLUSIONS
The noise channel model given by equation (2) provides a more realistic model of today’s
communication channels. In this manner, a more adaptive and sophisticated filtering system is
needed to restore video images. The proposed adaptive two-level filtering method, which
integrates an adaptive orientation selective filter and a median filter, was seen to effectively
enhance video images containing visible periodic noise lines.
The results obtained show us that the narrow-band noise signal can be detected and filtered by
mask adaptive frequency domain filtering and the slight deformations resulting from such
frequency domain filtering on the image data can be repaired by the median filtering.
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13. Baris Baykant ALAGOZ & Mehmet Emin TAGLUK
International Journal of Image Processing, (IJIP) Volume (5) : Issue (3) : 2011 282
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