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

作者：Chen, Yong-fei; Gao, Hong-xia; Wu, Zi-ling^*; Kang, Hui

来源：Optoelectronics Letters, 2018, 14(1): 57-60.

DOI：10.1007/s11801-018-7202-2

• Detail preservation
• Mixed noise removals
• Non-local similarities
• Quantitative measures
• Redundant transform
• Sparse reconstruction
• Sparse representation
• State-of-art methods

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.

• 出版日期2018-1-1
• 单位华南理工大学