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2-D Impulse Noise Suppression by Recursive Gaussian Maximum Likelihood Estimation

Author

Listed:
  • Yang Chen
  • Jian Yang
  • Huazhong Shu
  • Luyao Shi
  • Jiasong Wu
  • Limin Luo
  • Jean-Louis Coatrieux
  • Christine Toumoulin

Abstract

An effective approach termed Recursive Gaussian Maximum Likelihood Estimation (RGMLE) is developed in this paper to suppress 2-D impulse noise. And two algorithms termed RGMLE-C and RGMLE-CS are derived by using spatially-adaptive variances, which are respectively estimated based on certainty and joint certainty & similarity information. To give reliable implementation of RGMLE-C and RGMLE-CS algorithms, a novel recursion stopping strategy is proposed by evaluating the estimation error of uncorrupted pixels. Numerical experiments on different noise densities show that the proposed two algorithms can lead to significantly better results than some typical median type filters. Efficient implementation is also realized via GPU (Graphic Processing Unit)-based parallelization techniques.

Suggested Citation

  • Yang Chen & Jian Yang & Huazhong Shu & Luyao Shi & Jiasong Wu & Limin Luo & Jean-Louis Coatrieux & Christine Toumoulin, 2014. "2-D Impulse Noise Suppression by Recursive Gaussian Maximum Likelihood Estimation," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0096386
    DOI: 10.1371/journal.pone.0096386
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