19 July 2013 Image deblurring based structural graph and nonlocal similarity regularization
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Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887805 (2013) https://doi.org/10.1117/12.2030578
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China
Abstract
The distribution of image data points forms its geometrical structure. This structure characterizes the local variation, and provides valuable heuristics to the regularization of image restoration process. However, most of the existing approaches to sparse coding fail to consider this character of the image. In this paper, we address the deblurring problem of image restoration. We analyze the distribution of the input data points. Inspired by the theory of manifold learning algorithm, we build a k-NN graph to character the geometrical structure of the data, so the local manifold structure of the data can be explicitly taken into account. To enforce the invariance constraint, we introduce a patch-similarity based term into the cost function which penalizes the nonlocal invariance of the image. Experimental results have shown the effectiveness of the proposed scheme.
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Fangfang Jiang, Fangfang Jiang, Huahua Chen, Huahua Chen, Xueyi Ye, Xueyi Ye, } "Image deblurring based structural graph and nonlocal similarity regularization", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887805 (19 July 2013); doi: 10.1117/12.2030578; https://doi.org/10.1117/12.2030578
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