Open AccessA L_0 sparse analysis prior for blind poissonian image deconvolution
Author(s) -
Xiaojin Gong,
Baisheng Lai,
Zhiyu Xiang
Publication year - 2014
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.22.003860
Subject(s) - deconvolution , maximum a posteriori estimation , computer science , noise (video) , blind deconvolution , shot noise , algorithm , image (mathematics) , a priori and a posteriori , artificial intelligence , constraint (computer aided design) , poisson distribution , greedy algorithm , image restoration , sparse approximation , pattern recognition (psychology) , image processing , maximum likelihood , mathematics , statistics , telecommunications , philosophy , geometry , epistemology , detector
This paper proposes a new approach for blindly deconvolving images that are contaminated by Poisson noise. The proposed approach incorporates a new prior, that is the L0 sparse analysis prior, together with the total variation constraint into the maximum a posteriori (MAP) framework for deconvolution. A greedy analysis pursuit numerical scheme is exploited to solve the L0 regularized MAP problem. Experimental results show that our approach not only produces smooth results substantially suppressing artifacts and noise, but also preserves intensity changes sharply. Both quantitative and qualitative comparisons to the specialized state-of-the-art algorithms demonstrate its superiority.