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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Li, H., Zhang, Y., Duan, F., Zhu, Y. (2012). Blind Image Deblurring Based on Dictionary Replacing. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_46
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DOI: https://doi.org/10.1007/978-3-642-31919-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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