Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar patches to construct patch groups, recent studies have revealed that structural sparse representation (SSR) models can achieve promising performance in various image restoration tasks. However, most existing SSR methods only exploit the NSS prior from the input degraded (internal) image, and few methods utilize the NSS prior from external clean image corpus; how to jointly exploit the NSS priors of internal image and external clean image corpus is still an open problem. In this article, we propose a novel approach for image restoration by simultaneously considering internal and external nonlocal self-similarity (SNSS) priors that offer mutually complementary information. Specifically, we first group nonlocal similar patches from images of a training corpus. Then a group-based Gaussian mixture model (GMM) learning algorithm is applied to learn an external NSS prior. We exploit the SSR model by integrating the NSS priors of both internal and external image data. An alternating minimization with an adaptive parameter adjusting strategy is developed to solve the proposed SNSS-based image restoration problems, which makes the entire algorithm more stable and practical. Experimental results on three image restoration applications, namely image denoising, deblocking and deblurring, demonstrate that the proposed SNSS produces superior results compared to many popular or state-of-the-art methods in both objective and perceptual quality measurements.

中文翻译：

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通过同时非局部自相似先验进行图像恢复


最近的研究表明，通过利用图像非局部自相似性（NSS）先验，将相似的补丁聚类来构建补丁组，结构稀疏表示（SSR）模型可以在各种图像恢复任务中取得良好的性能。然而，大多数现有的SSR方法仅利用来自输入退化（内部）图像的NSS先验，很少有方法利用来自外部干净图像语料库的NSS先验；如何联合利用内部图像和外部干净图像语料库的 NSS 先验仍然是一个悬而未决的问题。在本文中，我们提出了一种新颖的图像恢复方法，通过同时考虑提供相互补充信息的内部和外部非局部自相似性（SNSS）先验。具体来说，我们首先对训练语料库图像中的非局部相似补丁进行分组。然后应用基于组的高斯混合模型（GMM）学习算法来学习外部NSS先验。我们通过集成内部和外部图像数据的 NSS 先验来开发 SSR 模型。开发了一种具有自适应参数调整策略的交替最小化来解决所提出的基于SNSS的图像恢复问题，这使得整个算法更加稳定和实用。三种图像恢复应用（即图像去噪、去块和去模糊）的实验结果表明，与许多流行或最先进的方法相比，所提出的 SNSS 在客观和感知质量测量方面都产生了优异的结果。