Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Volume 23, Issue 3, Pages 409-417Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2011.12.003
Keywords
Image processing; Blind deconvolution; Split Bregman; Total variation; Deblurring; Euler-Lagrange equation; Iteration scheme; Point spread function
Funding
- Ministry of Public Security of the People's Republic of China [2010YYCXCQSJ074]
- Natural Science Foundation of Chongqing Municipality of China [CSTC2009AB0175, CSTC2010BB2230]
- Fundamental Research Funds for the Central Universities [CDJXS11122221, CDJXS11122216]
Ask authors/readers for more resources
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. (C) 2011 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available