Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26312
Title: Sparsity-based image deblurring with locally adaptive and nonlocally robust regularization
Authors: Dong, W
Li, X
Zhang, D 
Shi, G
Keywords: Image deblurring
Iterative shrinkage
Nonlocal similarity
Sparsity-based local adaptation
Issue Date: 2011
Publisher: IEEE
Source: 2011 18th IEEE International Conference on Image Processing (ICIP), 11-14 September 2011, Brussels, p. 1841-1844 How to cite?
Abstract: Important structures in photographic images such as edges and textures are jointly characterized by local variation and nonlocal invariance (similarity). Both of them provide valuable heuristics to the regularization of image restoration process. In this pa per, we propose to explore two sets of complementary ideas: 1) locally learn PCA-based dictionaries and estimate the sparsity regularization parameters for each coefficient; and 2) nonlocally enforce the invariance constraint by introducing a patch-similarity based term into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image deblurring algorithm and its efficient implementation is discussed. Our experimental results have shown that the proposed scheme significantly outperforms several leading deblurring techniques in the literature on both objective and visual quality assessments.
URI: http://hdl.handle.net/10397/26312
ISBN: 978-1-4577-1304-0
978-1-4577-1302-6 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6115824
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