A Method Of Image Multiplicative Noise Removal Based On Weighted Nuclear Norm Minimization

In the field of image processing,multiplicative noise removal is a challenging image processing problem.Because a recorded image is the product of the original image and noise,most of the information of the original image is lost.The methods for multiplicative noise removal can be classified into two categories: the methods derived from the classical MAP Bayesian rule,and the ones that convert the multiplicative noise removal problem into additive noise removal problem.The numerical experimental results have shown that the additive noise is approximately Gaussian noise.Therefore,we can remove multiplicative noise in the logarithmic domain,by applying additive noise removal methods.In this paper,we construct a method for multiplicative noise removal method based on weighted nuclear norm minimization(WNNM)to remove image multiplicative noise.Taking into account the weighted nuclear of the method in the selection problem.Numerical experiments have shown that the model proposed in this paper is effective in removing multiplicative noise in images.Compared with existing methods of multiplicative noise removal,it has greatly improved both visual effect and denoising quality.