Weighted Non-locally Self-similarity Sparse Representation for Face Deblurring
The human face is one of the most interesting subjects in various computer vision tasks. In recent years, significant progress has been made for generic image deblurring problem, but existing popular sparse representation based deblurring methods are not able to achieve excellent results on blurry face images. The failure of these methods mainly stems from the lack of local/non-local self-similarity prior knowledge. There are many similar non-local patches in the neighborhood of a given patch in a face image, therefore, this property should be effectively exploited to obtain a good estimation of the sparse coding coefficients. In this paper, we introduce the current weighted non-locally self-similarity (WNLSS) method , which is originally proposed to remove the noise for natural images, into the face deblurring model. There are two terms in the WNLSS sparse representation model, data fidelity term and regularization term. Based on the theoretical analysis, we show the properties of data fidelity term and regularization term also can fit well for face deblurring problem. The results also demonstrate that WNLSS method can achieve excellent performance in terms of both synthetic and real blurred face dataset.
KeywordsFace Image Sparse Representation Local Patch Blur Kernel Weighted Code
The work presented in this paper was supported by the National Natural Science Foundation of China (Grants No. NSFC-61402046), Fund for Beijing University of Posts and Telecommunications (No.2013XZ10, 2013XD-04), Fund for the Doctoral Program of Higher Education of China (Grants No.20120005110002).
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