Heavy-tailed 잡음에 노출된 이미지에서의 비선형 잡음제거 알고리즘

Nonlinear Image Denoising Algorithm in the Presence of Heavy-Tailed Noise

The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to remove additive Gaussian noise contaminating images. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber's minimax norm. Our estimator is also optimal in the respect of maximizing the efficacy under the above noise environment.