Multiplicative Noise Removal Based on Unbiased Box-Cox Transformation (Communications in Computational Physics, 2017)
Multiplicative noise removal is a challenging problem in image restoration.In this paper, by applying Box-Cox transformation, we convert the multiplicative noiseremoval problem into the additive noise removal problem and the block matchingthree dimensional (BM3D) method is applied to get the final recovered image. Indeed,BM3D is an effective method to remove additive Gaussian white noise in images. Amaximum likelihood method is designed to determine the parameter in the Box-Coxtransformation. We also present the unbiased inverse transform for the Box-Cox transformationwhich is important. Both theoretical analysis and experimental results illustrateclearly that the proposed method can remove multiplicative noise very wellespecially when multiplicative noise is heavy. The proposed method is superior to theexisting methods for multiplicative noise removal in the literature.