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An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal

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【作者】 陈勇翡高红霞吴梓灵康慧

【Author】 CHEN Yong-fei;GAO Hong-xia;WU Zi-ling;KANG Hui;School of Automation Science and Engineering, South China University of Technology;Guangdong Polytechnic Normal University;

【机构】 School of Automation Science and Engineering, South China University of TechnologyGuangdong Polytechnic Normal University

【摘要】 Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.

【Abstract】 Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.

【基金】 supported by the National Natural Science Foundation of China(Nos.61403146 and 61603105);the Fundamental Research Funds for the Central Universities(No.2015ZM128);the Science and Technology Program of Guangzhou in China(Nos.201707010054 and 201704030072)
  • 【文献出处】 Optoelectronics Letters ,光电子快报(英文版) , 编辑部邮箱 ,2018年01期
  • 【分类号】TP391.41
  • 【被引频次】1
  • 【下载频次】42
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