Poissonian image deconvolution with analysis sparsity priors

Houzhang Fang; Luxin Yan

doi:10.1117/1.JEI.22.2.023033

24 June 2013 Poissonian image deconvolution with analysis sparsity priors

Houzhang Fang, Luxin Yan

Author Affiliations +

Funded by: National Natural Science Foundation of China

Journal of Electronic Imaging, Vol. 22, Issue 2, 023033 (June 2013). https://doi.org/10.1117/1.JEI.22.2.023033

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.

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Houzhang Fang and Luxin Yan "Poissonian image deconvolution with analysis sparsity priors," Journal of Electronic Imaging 22(2), 023033 (24 June 2013). https://doi.org/10.1117/1.JEI.22.2.023033

Published: 24 June 2013

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