A novel MR image denoising via LRMA and NLSS
A novel MR image denoising via LRMA and NLSS
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DOI：
10.1016/j.sigpro.2021.108109
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Zhen Chen;Yuli Fu;Youjun Xiang;Yin-Lin Zhu
中科院分区：
文献类型：
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作者：
Zhen Chen;Yuli Fu;Youjun Xiang;Yin-Lin Zhu
非局部自相似性已被证明是一个有用的工具，图像去噪。对于MR图像去噪，结合非局部自相似性和低秩近似的方法，由于其良好的性能，近年来受到了广泛的关注。由于原低秩逼近问题难以求解，常用的方法是利用核范数极小化进行矩阵低秩逼近。然而，通过核范数最小化得到的解一般偏离原问题的解。本文提出了一种新的非局部自相似算法和低秩近似算法相结合的MR图像去噪方法。在所提出的方法中，一个相似性评价相对于噪声提出了在补丁匹配阶段。为了逼近原始的低秩最小化问题，该方法在每一步最小化基于迹的算子。每一个极小化都是可解的，并用来近似原始的低秩极小化。建立了一个算法，以及这种近似。实验结果表明，与一些低阶近似方法相比，该方法在客观质量度量和视觉检测方面均具有上级性能.
Nonlocal self-similarity has been proven to be a useful tool for image denoising. For MR image denoising, the method combining the nonlocal self-similarity with the low-rank approximation has been recently attracting considerable attentions, due to its favorable performance. Since the original low-rank approximation problem is difficult to be solved, the frequently used method is to use the nuclear norm minimization for the matrix low-rank approximation. However, the solution obtained by nuclear norm minimization generally deviates from the solution of the original problem. In this paper, an approach for MR image denoising is proposed by combining a novel nonlocal self-similarity scheme with a novel low-rank approximation scheme. In proposed approach, a similarity evaluation with respect to the noise is proposed in the patch matching stage. To approximate the original low-rank minimization problem, the propose approach minimizes trace-based operator at each step. Every minimization is solvable and used to approximate the original low-rank minimization. An algorithm is established for this approximation, as well. Experimental results show that the proposed approach has a superior performance, comparing with some of the low-rank approximation methods, in both the objective quality metrics and visual inspections.