Multiplicative noise removal is a challenging problem in image restoration. In this paper, by applying Box-Cox transformation, we convert the multiplicative noise removal problem into the additive noise removal problem and the block matching three 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. A maximum likelihood method is designed to determine the parameter in the Box-Cox transformation. We also present the unbiased inverse transform for the Box-Cox transformation which is important. Both theoretical analysis and experimental results illustrate clearly that the proposed method can remove multiplicative noise very well especially when multiplicative noise is heavy. The proposed method is superior to the existing methods for multiplicative noise removal in the literature.

This study investigates the determinants of crawfish consumption in South Louisiana using a generalized limited dependent variable model that accounts for both participation and consumption decisions. Income, Catholic, white, and household size increase the likelihood of crawfish consumption but not the conditional level of consumption. Education and employment status are among the other household characteristics that determine the conditional level of consumption.