期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 12, 页码 5127-5139出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2478407
关键词
Blind deconvolution; image deblurring; variational distribution approximations; Dirichlet distribution; constrained optimization; point spread function; inverse problem
资金
- U.S. Department of Energy [DE-NA0002520]
- National Natural Science Foundation of China [61233005]
- Ministerio de Ciencia e Innovacion [TIN2013-43880-R]
- European Regional Development Fund
- CEI BioTic through the Universidad de Granada
- Chinese Scholarship Council
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a linear inverse problem with normalization and nonnegative constraints must be solved. However, the normalization constraint is ignored in many blind image deblurring methods, mainly because it makes the problem less tractable. In this paper, we show that the normalization constraint can be very naturally incorporated into the estimation process by using a Dirichlet distribution to approximate the posterior distribution of the blur. Making use of variational Dirichlet approximation, we provide a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel. Experiments with synthetic and real data demonstrate that the proposed method is very competitive to the state-of-the-art blind image restoration methods.
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