2-D impulse noise suppression by recursive gaussian maximum likelihood estimation
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 jo...
Saved in:
Published in | PloS one Vol. 9; no. 5; p. e96386 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
16.05.2014
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: LL YC JY. Performed the experiments: YC LS JY JW. Analyzed the data: JY YC JC HS. Contributed reagents/materials/analysis tools: LL YC LS. Wrote the paper: YC JY CT LL. Competing Interests: The authors have declared that no competing interests exist. |
ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0096386 |