Poisson noise removal via learned dictionary

Yu Xiao*, Tieyong ZENG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

In this paper, we address the restoration of images corrupted by Poisson noise. The proposed new model contains two terms: one is from the sparse representation of the transformed image via variance stabilizing transformation (VST); the other is a data-fidelity term caused by the statistical properties of Poisson noise. The main algorithm is efficient. We first learn a dictionary to sparsely represent the transformed image using a state-of-the-art dictionary learning method, and then solve the minimization of the variational form by Newton method. Comparative experiments are carried out to show the leading performance of our new model.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages1177-1180
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period26/09/1029/09/10

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

User-Defined Keywords

  • Dictionary learning
  • Image denoising
  • Poisson noise
  • Sparse representations
  • Variance stabilizing transformation

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