Group Sparsity Mixture Model and Its Application on Image Denoising-Reference-Cited by-同舟云学术

Group Sparsity Mixture Model and Its Application on Image Denoising

Published:2022 Issue: Volume:31 Page:5677-5690
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Liu Haosen¹^ORCID,Li Laquan²,Lu Jiangbo³^ORCID,Tan Shan¹^ORCID

Affiliation:

1. Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

2. School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China

3. SmartMore Corporation Ltd., Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9626658/09846878.pdf?arnumber=9846878

Reference38 articles.

1. An Iterative Regularization Method for Total Variation-Based Image Restoration

2. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

3. Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

4. Image Restoration via Simultaneous Nonlocal Self-Similarity Priors

5. Group Sparsity Residual Constraint With Non-Local Priors for Image Restoration

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