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Journal of Electronic Imaging

Poissonian image deconvolution with analysis sparsity priors
Author(s): Houzhang Fang; Luxin Yan
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Paper Abstract

Deconvolving Poissonian image has been a significant subject in various application areas such as astronomical, microscopic, and medical imaging. In this paper, a regularization-based approach is proposed to solve Poissonian image deconvolution by minimizing the regularization energy functional, which is composed of the generalized Kullback-Leibler divergence as the data-fidelity term and sparsity prior constraints as the regularization term, and a non-negativity constraint. We consider two sparsity prior constraints which include framelet-based analysis prior and combination of framelet and total variation analysis priors. Furthermore, we show that the resulting minimization problems can be efficiently solved by the split Bregman method. The comparative experimental results including quantitative and qualitative analysis manifest that our algorithm can effectively remove blur, suppress noise, and reduce artifacts.

Paper Details

Date Published: 24 June 2013
PDF: 11 pages
J. Electron. Imag. 22(2) 023033 doi: 10.1117/1.JEI.22.2.023033
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Houzhang Fang, Huazhong Univ. of Science and Technology (China)
Luxin Yan, Huazhong Univ. of Science and Technology (China)

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