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Patch-Based Low-Rank Minimization for Image Denoising  [PDF]
Haijuan Hu,Jacques Froment,Quansheng Liu
Computer Science , 2015,
Abstract: Patch-based sparse representation and low-rank approximation for image processing attract much attention in recent years. The minimization of the matrix rank coupled with the Frobenius norm data fidelity can be solved by the hard thresholding filter with principle component analysis (PCA) or singular value decomposition (SVD). Based on this idea, we propose a patch-based low-rank minimization method for image denoising, which learns compact dictionaries from similar patches with PCA or SVD, and applies simple hard thresholding filters to shrink the representation coefficients. Compared to recent patch-based sparse representation methods, experiments demonstrate that the proposed method is not only rather rapid, but also effective for a variety of natural images, especially for texture parts in images.
Wavelet Based Image Denoising Technique  [PDF]
Sachin D Ruikar,Dharmpal D Doye
International Journal of Advanced Computer Sciences and Applications , 2011,
Abstract: This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poissona€ s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.
Optimal Denoising Of an Image Using Anscombe Transformation Based Image Stabilization
Sophia Comaneci.J,K.John Peter
International Journal of Engineering and Advanced Technology , 2012,
Abstract: This paper proposes an effective inversing of the anscombe transformation with the help of adaptive bilateral image denoising algorithm. The Poisson noise removal is carried out into three steps. They are First, Image pre-processin,. Second, image denoising and Third, Image retrieval. In image pre-processing the images of any format can be got as input they are then converted into gray scale images for ease of functions and this paper uses anscombe transform to stabilize the image to a constant intensity level. This is very helpful in determining the noise at low counts. For image denoising, Multiscale variance stabilizing transform is the technique that is proposed to denoise the image. Now the noisy pixels in the images are removed. This paper also proposes a similar neighborhood function that is essential for filling the noisy pixels with the help of non-local means of similar neighbors. This is suitable for overall adjustment of the image. But in the case of texture images this technique is not applicable and in that condition the technique proposed is bilateral transformation of texture images. For this we use Bilateral image denoising and PCA analysis. This paper also proposes an approach to determine the best among the two processes in terms of performance and efficiency. Next step is very crucial because the application of inverse transformation is an critical factor. The inverse transform that is proposed in this paper is minimum mean square error method. This results in retrieval of an image with efficient filtering and inversing functions.
Diffusion Weighted Image Denoising Using Overcomplete Local PCA  [PDF]
José V. Manjón, Pierrick Coupé, Luis Concha, Antonio Buades, D. Louis Collins, Montserrat Robles
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0073021
Abstract: Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.
An Improved Image Denoising Method Based on Wavelet Thresholding  [PDF]
Hari Om, Mantosh Biswas
Journal of Signal and Information Processing (JSIP) , 2012, DOI: 10.4236/jsip.2012.31014
Abstract: VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.
Medical Image Denoising Using Adaptive Threshold Based on Contourlet Transform
S.Satheesh,KVSVR Prasad
Advanced Computing : an International Journal , 2011,
Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic ResonanceImaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform.Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio(PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.
Medical Image Denoising using Adaptive Threshold Based on Contourlet Transform  [PDF]
S. Satheesh,KVSVR Prasad
Computer Science , 2011, DOI: 10.5121/acij.2011.2205
Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform. Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio (PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.
Wavelet Based Image Denoising Using Adaptive Subband Thresholding
S. Sudha,G.R. Suresh,R. Sukanesh
International Journal of Soft Computing , 2012,
Abstract: This study proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding based on the Generalized Gaussian Distribution (GGD) widely used in image processing applications. The proposed threshold is simple and it is adaptive to each sub band because it depends on data-driven estimates of the parameters. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, variance. Our method describes a new method for suppression of noise in image by fusing the wavelet denoising technique with optimized thresholding function improving the denoised results significantly. Simulated noise images are used to evaluate the denoising performance of proposed algorithm along with another wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the PSNR and the prevented edge information in most cases. We have compared this with various denoising methods like wiener filter, VisuShrink and BayesShrink.
Directional Total Variation Filtering Based Image Denoising Method  [PDF]
S.Karthik,Hemanth V K,K.P. Soman,V.Balaji
International Journal of Computer Science Issues , 2012,
Abstract: In this paper, we study signal denoising technique based on total variation (TV) which was reported by Ivan W. Selesnick, Ilker Bayram [1]. Here, we present a directional total variation algorithm for image denoising. In most of the image denoising methods, the total variation denoising is directly performed on the noisy images. In this work, we apply a 1D TV denoising algorithm in sequential manner on the pixel sequence obtained in different orientations including zig-zag, horizontal and vertical. The performance of the proposed method is evaluated using the standard test images and the quality of the denoised images is assessed using various objective metrics such as peak signal to noise ratio (PSNR), structural similarity index metrics (SSIM), visual signal to noise ratio (VSNR). Various experiments show that the proposed method provides promising results with less computational load.
A Novel Image Denoising Algorithm Based on Riemann-Liouville Definition  [cached]
Jinrong HU,Yifei Pu,Jiliu Zhou
Journal of Computers , 2011, DOI: 10.4304/jcp.6.7.1332-1338
Abstract: In this paper, a novel image denoising algorithm named fractional integral image denoising algorithm (FIIDA) is proposed, which based on fractional calculus Riemann-Liouville definition. The structures of n*n fractional integral masks of this algorithm on the directions of 135 degrees, 90 degrees, 45 degrees, 0 degrees, 180 degrees, 315 degrees, 270 degrees and 225 degrees are constructed and discussed. The denoising performance of FIIDA is measured using experiments according to subjective and objective standards of visual perception and PSNR values. The simulation results show that the FIIDA’s performance is prior to the Gaussian smoothing filter, especially when the noise standard deviation is less than 30.
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