groupsparsecoding（GSC） is a powerful mechanism that has achieved great success in many low-level vision tasks,showing great potential in image *** groupsparsecoding generally uses overcomplete dictionaries and l...
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groupsparsecoding（GSC） is a powerful mechanism that has achieved great success in many low-level vision tasks,showing great potential in image *** groupsparsecoding generally uses overcomplete dictionaries and l-norm to regularize sparse *** this is only an estimate of the solution,which cannot obtain a sparse solution and has a high computational *** this paper,we use a GSC framework with adaptive dictionary learning for image *** order to improve the accuracy of obtaining sparse coefficients,the dictionary used in this paper is learned from the input image,which can be obtained by applying SVD once for each patch *** use ADMM algorithm to solve the objective *** results show that the PSNR value of our approach not only is competitive with many advanced image denoising methods but also achieves better visual effects.
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by usi...
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ISBN:
(纸本)9781538646588
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the groupsparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by usi...
详细信息
ISBN:
(纸本)9781538646595
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the groupsparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.
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