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首页> 外文期刊>Journal of visual communication & image representation >Denoising by low-rank and sparse representations
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Denoising by low-rank and sparse representations

机译：通过低秩和稀疏表示去噪

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摘要

Due to the ill-posed nature of image denoising problem, good image priors are of great importance for an effective restoration. Nonlocal self-similarity and sparsity are two popular and widely used image priors which have led to several state-of-the-art methods in natural image denoising. In this paper, we take advantage of these priors and propose a new denoising algorithm based on sparse and low-rank representation of image patches under a nonlocal framework. This framework consists of two complementary steps. In the first step, noise removal from groups of matched image patches is formulated as recovery of low-rank matrices from noisy data. This problem is then efficiently solved under asymptotic matrix reconstruction model based on recent results from random matrix theory which leads to a parameter free optimal estimator. Nonlocal learned sparse representation is adopted in the second step to suppress artifacts introduced in the previous estimate. Experimental results, demonstrate the superior denoising performance of the proposed algorithm as compared with the state-of-the-art methods. (C) 2016 Elsevier Inc. All rights reserved.
机译：由于图像去噪问题的不适定性，良好的图像先验对有效恢复至关重要。非局部自相似性和稀疏性是两种流行且广泛使用的图像先验，它们导致了自然图像去噪中的几种最新技术。在本文中，我们利用这些先验优势，提出了一种基于非局部框架下图像块稀疏和低秩表示的新去噪算法。该框架包括两个互补步骤。第一步，将匹配图像块组中的噪声去除公式化为从嘈杂数据中恢复低秩矩阵。然后根据渐近矩阵重构模型有效地解决了这个问题，该模型基于随机矩阵理论的最新结果，这导致了无参数的最优估计器。第二步采用非本地学习的稀疏表示，以抑制先前估计中引入的伪像。实验结果表明，与最新方法相比，该算法具有更好的去噪性能。 （C）2016 Elsevier Inc.保留所有权利。

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