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...

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Published inPloS one Vol. 9; no. 5; p. e96386
Main Authors Chen, Yang, Yang, Jian, Shu, Huazhong, Shi, Luyao, Wu, Jiasong, Luo, Limin, Coatrieux, Jean-Louis, Toumoulin, Christine
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 16.05.2014
Public Library of Science (PLoS)
Subjects
Algorithms
Bioengineering
Computer networks
Education
Engineering and Technology
Engineering Sciences
Estimation errors
Graphics processing units
Image Processing, Computer-Assisted - methods
Imaging
Laboratories
Life Sciences
Likelihood Functions
Maximum likelihood estimation
Maximum likelihood method
Models, Theoretical
Noise
Noise reduction
Numerical experiments
Parallel processing
Recursive methods
Science
Signal and Image processing
Signal processing
Signal-To-Noise Ratio
Beijing China
New York
France
United States > US
China
REMOVAL
MEDIAN FILTER
TOMOGRAPHIC RECONSTRUCTION
DETECTOR
ALGORITHM
WEIGHTS
SIMILARITY
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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
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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