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12-11-2017 | Issue 3/2018

Journal of Scientific Computing 3/2018

A Fast Algorithm for Deconvolution and Poisson Noise Removal

Journal:
Journal of Scientific Computing > Issue 3/2018
Authors:
Xiongjun Zhang, Michael K. Ng, Minru Bai
Important notes
Xiongjun Zhang: The research of this author was supported in part by the Fundamental Research Funds for the Central Universities under Grant 230-20205170463-610.
Michael K. Ng: The research of this author was supported in part by the HKRGC GRF 1202715, 12306616, 12200317 and HKBU RC-ICRS/16-17/03.
Minru Bai: The research of this author was supported in part by the National Science Foundation of China under Grant 11571098.

Abstract

Poisson noise removal problems have attracted much attention in recent years. The main aim of this paper is to study and propose an alternating minimization algorithm for Poisson noise removal with nonnegative constraint. The algorithm minimizes the sum of a Kullback-Leibler divergence term and a total variation term. We derive the algorithm by utilizing the quadratic penalty function technique. Moreover, the convergence of the proposed algorithm is also established under very mild conditions. Numerical comparisons between our approach and several state-of-the-art algorithms are presented to demonstrate the efficiency of our proposed algorithm.

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