Yumei Huang, Professor, School of Mathematics and Statistics, Lanzhou University

Zhongzhi Bai, Professor

Methods for Multiplicative Noise Removal

11:00-12:00 September 17(Saturday), Tencent Meeting ID: 331-973-588

Multiplicative noise removal is a challenging image processing problem. In this talk, we consider the methods for image multiplicative noise removal. The maximum a posteriori formulation based methods and the logarithmic transformation of multiplicative denoising problems into additive denoising problems are important methods for multiplicative noise removal. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we proposed to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. In addition, we also proposed the low rank minimization algorithm to remove multiplicative noise in images. Extensive experimental results suggest that the proposed algorithm outperforms state-of-the-art methods.