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A Study on Nonlinear Noise Removal for Images Corrupted with ${\alpha}$-Stable Random Noise  

Hahn, Hee-Il (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.44, no.6, 2007 , pp. 93-99 More about this Journal
Abstract
Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.
Keywords
maximum likelihood estimator; heavy-tailed noise distribution; ${\alpha}$-stable random noise;
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