Image Patch Transform Training and Non-convex Regularization for Image Denoising and Deblurring

被引：0

作者：

Yang P. ^{[1
]}

Zhao Y. ^{[1
]}

Zheng J. ^{[1
]}

Wang W. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Bregman Split Iteration; Deblurring; Denoising; Dictionary Learning; Non-convex Optimization; Transform Learning;

D O I：

10.16451/j.cnki.issn1003-6059.201910006

中图分类号：

学科分类号：

摘要：

Aiming at insufficient sampling of image patches in the process of over-complete dictionary training of sparse representation model, an algorithm of image patch transform training and non-convex regularization for image denoising and deblurring is proposed. The image patch search strategy with inter-group variance constraint is adopted, and the selected dictionary set is transposed and learned according to the adaptive soft threshold. The lp(0<p<1) norm is adopted in the reconstruction process to ensure strong sparsity of the results. Split Bregman method is employed to solve the proposed non-convex model. Experimental results show that the proposed algorithm produces better visual effect and Denoising and Deblurring effect. © 2019, Science Press. All right reserved.

引用

页码：917 / 926

页数：9

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