Total variation blind deconvolution employing split Bregman iteration

Authors:
Weihong Li;Quanli Li;Weiguo Gong;Shu Tang
Affiliations:
Key Lab of Optoelectronic Technology and Systems of Education Ministry, Chongqing University, Chongqing 400044, China;Key Lab of Optoelectronic Technology and Systems of Education Ministry, Chongqing University, Chongqing 400044, China;Key Lab of Optoelectronic Technology and Systems of Education Ministry, Chongqing University, Chongqing 400044, China;Key Lab of Optoelectronic Technology and Systems of Education Ministry, Chongqing University, Chongqing 400044, China
Venue:
Journal of Visual Communication and Image Representation
Year:
2012

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Abstract

Blind image deconvolution is one of the most challenging problems in image processing. The total variation (TV) regularization approach can effectively recover edges of image. In this paper, we propose a new TV blind deconvolution algorithm by employing split Bregman iteration (called as TV-BDSB). Considering the operator splitting and penalty techniques, we present also a new splitting objective function. Then, we propose an extended split Bregman iteration to address the minimizing problems, the latent image and the blur kernel are estimated alternately. The TV-BDSB algorithm can greatly reduce the computational cost and improve remarkably the image quality. Experiments are conducted on both synthetic and real-life degradations. Comparisons are also made with some existing blind deconvolution methods. Experimental results indicate the advantages of the proposed algorithm.