Signal overcomplete representation and sparse decomposition based on redundant dictionaries

2005 ◽  
Vol 50 (23) ◽  
pp. 2672-2677 ◽  
Author(s):  
Zhang Chunmei ◽  
Yin Zhongke ◽  
Chen Xiangdong ◽  
Xiao Mingxia
Keyword(s):  
Sparse Decomposition ◽  
Overcomplete Representation ◽  
Redundant Dictionaries

New self-adaptive method for image denoising based on sparse decomposition and clustering

2013 ◽  
Vol 33 (2) ◽  
pp. 476-479
Author(s):  
Yali WEI ◽  
Xianbin WEN ◽  
Yongliao ZOU ◽  
Yongchun ZHENG
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sparse Decomposition ◽  
Self Adaptive

scholarly journals Image smoothing based on global sparsity decomposition and a variable parameter

2021 ◽  
Author(s):  
Xiang Ma ◽  
Xuemei Li ◽  
Yuanfeng Zhou ◽  
Caiming Zhang
Keyword(s):  
Removal Rate ◽  
Variable Parameter ◽  
Smoothing Parameter ◽  
Smoothing Effect ◽  
Image Smoothing ◽  
Sparse Decomposition ◽  
Alternating Direction ◽  
Texture Removal ◽  
Ideal Algorithm ◽  
Better Than

AbstractSmoothing images, especially with rich texture, is an important problem in computer vision. Obtaining an ideal result is difficult due to complexity, irregularity, and anisotropicity of the texture. Besides, some properties are shared by the texture and the structure in an image. It is a hard compromise to retain structure and simultaneously remove texture. To create an ideal algorithm for image smoothing, we face three problems. For images with rich textures, the smoothing effect should be enhanced. We should overcome inconsistency of smoothing results in different parts of the image. It is necessary to create a method to evaluate the smoothing effect. We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems. A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter. Three evaluation measures: edge integrity rate, texture removal rate, and gradient value distribution are proposed to cope with the third problem. We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results. Experiments show that our algorithm is better than existing algorithms both visually and quantitatively. We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior

2020 ◽  
Vol 523 ◽  
pp. 14-37 ◽  
Author(s):  
Huafeng Li ◽  
Xiaoge He ◽  
Zhengtao Yu ◽  
Jiebo Luo
Keyword(s):  
Image Fusion ◽  
Low Rank ◽  
Sparse Decomposition ◽  
Robust Image ◽  
Noise Robust

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.

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