A dictionary learning approach for Poisson image Deblurring

Liyan Ma, Lionel Moisan, Jian Yu, Tieyong Zeng

Research output: Contribution to journalJournal articlepeer-review

94 Citations (Scopus)


The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing. While most existing methods are based on variational models, generally derived from a maximum a posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we propose in this paper a model containing three terms: a patch-based sparse representation prior over a learned dictionary, the pixel-based total variation regularization term and a data-fidelity term capturing the statistics of Poisson noise. The resulting optimization problem can be solved by an alternating minimization technique combined with variable splitting. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio value and the method noise, the proposed algorithm outperforms state-of-the-art methods.

Original languageEnglish
Article number6490410
Pages (from-to)1277-1289
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number7
Publication statusPublished - Jul 2013

Scopus Subject Areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Deblurring
  • dictionary learning
  • patch-based approach
  • Poisson noise
  • total variation


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