| CHEN Yong-fei,GAO Hong-xia,WU Zi-ling,KANG Hui.An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal[J].Optoelectronics Letters,2018,14(1):57-60 |
| An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal |
| Author Name | Affiliation | | CHEN Yong-fei | School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China | | GAO Hong-xia | School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China | | WU Zi-ling | School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China | | KANG Hui | Guangdong Polytechnic Normal University, Guangzhou 510665, China |
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| 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. |
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