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An Image Restoration Method with Independently Local Dictionary Learning

机译：具有独立本地文本学习的图像恢复方法

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Background: Recently, sparse representation has been significantly used in various imageinverse problems, such as image deblurring, super resolution and compressive sensing, and has shownpromising results. The key issue of sparse representation is how to find a reasonable dictionary, bywhich the image can present more sparsity, as described in various patents.Method: In this paper, we address the image restoration and propose a novel cost function. Consideringthe significant difference of underlying structure within different patches, we independently trainthe dictionary using a set of self-similarity patches to present each patch more sparsely.Result: To solve the proposed cost function, an approach based on alternating optimization is presentedto obtain the approximate solution.Conclusion: Experimental results demonstrate that the proposed method is superior to many existingexcellent algorithms.
机译：背景：最近，稀疏表示在各种ImageInverse问题中被显着使用，例如图像去孔，超分辨率和压缩感测，并具有出示的结果。 稀疏表示的关键问题是如何找到合理的字典，通过各种专利中所述，图像可以呈现更多的稀疏性。在本文中，我们解决了图像恢复并提出了一种新的成本函数。 考虑到不同补丁中的底层结构的显着差异，我们独立地使用一组自相似贴片来呈现每个修补程序更加稀疏。结果：要解决所提出的成本函数，提出了一种基于交替优化的方法来获得近似解 结论：实验结果表明，所提出的方法优于许多现有的算法。

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