Multiplicative Noise Removal with A Non-Convex Optimization ModelMS41

In this talk, we introduce a variational restoration model for multiplicative noise reduction. Different from popular existing models which focus on pursuing convexity, the proposed sparsity-aware model may be nonconvex depending on the conditions of the parameters of the model for achieving the optimal denoising performance. An algorithm for finding a critical point of the objective function of the model is developed. Experimental results show that the proposed method can remarkably outperform several state-of-art methods.

This presentation is part of Minisymposium “MS41 - Framelets, Optimization, and Image Processing (3 parts)
organized by: Xiaosheng Zhuang (City University of Hong Kong) , Lixin Shen (Syracuse University) , Bin Han (University of Alberta) , Yan-Ran Li (Shenzhen Univeristy) .

Jian Lu (Shenzhen University)
image deblurring, image enhancement, image reconstruction, image representation, partial differential equation models