Blind image deblurring based on dictionary replacing

Authors:
Haisen Li;Yanning Zhang;Feng Duan;Yu Zhu
Affiliations:
Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;Shaanxi Key Laboratory of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

Traditional image deblurring is based on deconvolution, an ill-posed problem, which is sensitive to the accuracy of the blur kernel. In this paper, we propose a blind image deblurring method based on dictionary replacing. First, we estimate the blur kernel from the blur image , and then based on the sparse representation of the image patch under over-complete dictionary, we deblur the image via replacing blur dictionary with clear dictionary. Our method avoids the deconvolution problem and can bring more high-frequency information in the deblurred image via dictionary replacing. Experimental results compared with state-of-the-art blind deblurring methods demonstrate the effectiveness of the proposed method.