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 insp...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.展开更多
基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入Cosparse解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对...基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入Cosparse解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对整幅图像进行稀疏表示,从而提出一种新的图像复原算法。该算法将图像复原问题归结为双稀疏正则化问题。为求解复杂的双稀疏优化问题,本文运用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)算法将该约束优化问题分解为若干子问题,通过交替迭代求解获得复原图像。实验中对不同类型的模糊图像进行了复原,其结果表明该算法对于各类模糊图像的复原比现有复原算法效果更好,从而验证了算法的有效性。展开更多
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)
文摘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.
文摘基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入Cosparse解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对整幅图像进行稀疏表示,从而提出一种新的图像复原算法。该算法将图像复原问题归结为双稀疏正则化问题。为求解复杂的双稀疏优化问题,本文运用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)算法将该约束优化问题分解为若干子问题,通过交替迭代求解获得复原图像。实验中对不同类型的模糊图像进行了复原,其结果表明该算法对于各类模糊图像的复原比现有复原算法效果更好,从而验证了算法的有效性。
