Image Denoising Algorithm Based on Nonlocal Regularization Sparse Representation

2020 ◽  
Vol 20 (20) ◽  
pp. 11943-11950
Author(s):  
Hongchun Du
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Nonlocal Regularization

scholarly journals Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation

2017 ◽  
Vol 7 (5) ◽  
pp. 436 ◽  
Author(s):  
Hai Wang ◽  
Xue Xiao ◽  
Xiongyou Peng ◽  
Yan Liu ◽  
Wei Zhao
Keyword(s):  
Sparse Representation ◽  
Image Denoising

Algorithm of Noise Reduction for Adaptive Dictionary Learning Research Based on Brain MRI Images

2014 ◽  
Vol 687-691 ◽  
pp. 4123-4127 ◽  
Author(s):  
Jia Qing Miao
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Brain Mri ◽  
Image Representation ◽  
Training Algorithm ◽  
Redundant Dictionary ◽  
Popular Method ◽  
Sparse Decomposition ◽  
Learning Research ◽  
Dictionary Training

Recent years, the image sparse representation has been the popular method in the study of image representation, which has put forward a new idea in the image denoising. Its basic principle is that the original image has the sparse representation under the proper over-complete dictionary. Filter out the noise, we should find out the sparse representation of the image through the design of the dictionary. Its mechanism is that one hand the useful information of the image would be effectively expressed because of the sparse decomposition algorithm based on the redundant dictionary. The other the noise would not be expressed through the dictionary atoms. We do the image denoising according to the image sparse representation. Because of the superiority of the adaptive dictionary algorithm in the image, in this paper, we discuss the over-complete dictionary training algorithm. And we prove the effectiveness through the MATLAB.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Image denoising via correlation-based sparse representation

2017 ◽  
Vol 11 (8) ◽  
pp. 1501-1508 ◽  
Author(s):  
Gulsher Baloch ◽  
Huseyin Ozkaramanli
Keyword(s):  
Sparse Representation ◽  
Image Denoising

Image Denoising Based on Sparse Representation over Learned Dictionaries

2019 ◽  
pp. 479-486
Author(s):  
Juan Wang ◽  
Guanghui Li ◽  
Fei Du ◽  
Meng Wang ◽  
Yong Hu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Denoising

scholarly journals Image mixed gaussian and impulse noise elimination based on sparse representation model

2021 ◽  
Vol 23 (3) ◽  
pp. 1440
Author(s):  
Ahmed Abdulqader Hussein ◽  
Sabahaldin A. Hussain ◽  
Ahmed Hameed Reja
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Impulse Noise ◽  
Heavy Tails ◽  
Noise Distribution ◽  
Self Similarity ◽  
Noise Elimination ◽  
Variational Framework ◽  
Mixed Noise ◽  
State Of Art

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

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