As a prior knowledge, non-local self-similarity (NSS) has been widely utilised in ill-posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch-based methods that only explore NSS in the same scale, a multi-scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi-scale strategy to expand the search space of block-matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi-scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS-based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.

中文翻译：

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使用加权核范数最小化的基于多尺度补丁的图像去噪

作为先验知识，非局部自相似性（NSS）已广泛应用于不适定问题。实际上，相似的纹理不仅以单个比例出现，而且以不同比例出现。不同于大多数现有的仅基于补丁的NSS的基于补丁的方法，本研究提出了一种基于多尺度补丁的图像去噪算法。作者设计了一种多尺度策略来扩展块匹配的搜索空间，这将增加找到更多相似补丁的可能性。之后，采用加权核规范最小化（WNNM）算法来揭示潜在的干净补丁。通过加入多尺度框架，可以提高WNNM的性能。该算法可用于解决基于NSS的图像恢复任务。在这项研究中，主要研究图像去噪，