A Fast Algorithm for Deconvolution and Poisson Noise Removal

Xiongjun Zhang*, Kwok Po NG, Minru Bai

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1535-1554
Number of pages20
JournalJournal of Scientific Computing
Issue number3
Publication statusPublished - 1 Jun 2018

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Numerical Analysis
  • Engineering(all)
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Alternating minimization algorithm
  • Kullback-Leibler divergence
  • Poisson noise
  • Total variation


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