DocumentCode
1552383
Title
Image denoising: a nonlinear robust statistical approach
Author
Ben Hamza, A. ; Krim, Hamid
Author_Institution
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Volume
49
Issue
12
fYear
2001
fDate
12/1/2001 12:00:00 AM
Firstpage
3045
Lastpage
3054
Abstract
Nonlinear filtering techniques based on the theory of robust estimation are introduced. Some deterministic and asymptotic properties are derived. The proposed denoising methods are optimal over the Huber ε-contaminated normal neighborhood and are highly resistant to outliers. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated
Keywords
Gaussian noise; filtering theory; image processing; median filters; nonlinear filters; parameter estimation; statistical analysis; LogCauchy filter; asymptotic properties; deterministic properties; image denoising methods; mean-median filter; mean-relaxed median filter; noise reduction performance; nonlinear filtering; robust estimation; statistical properties; Additive noise; Atmospheric modeling; Estimation theory; Filtering theory; Gaussian noise; Image denoising; Noise reduction; Noise robustness; Nonlinear filters; Probability distribution;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.969512
Filename
969512
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