Since digital images are usually sparse in the wavelet frame domain, some nonconvex minimization models based on wavelet frame have been proposed and sparse approximations have been widely used in image restoration in recent years. Some proximal alternating iterative hard thresholding methods are proposed in this talk to solve the nonconvex model based on wavelet frame to restore degraded image. We will perform the test on image denoising and image deconvolution.

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) .

Authors:

Yi Shen (Zhejiang Sci-Tech Univeristy)

Keywords:

image deblurring, image reconstruction, image representation, inverse problems, nonlinear optimization