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
fDate :
12/1/2001 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
