Alternating method based on frameletl0-norm and TV regularization for image restoration
Jingjing Liu, Guoxi Ni, Shaowen Yan · Inverse Problems in Science and Engineering · 2018
This paper presents an efficient alternating method for image deblurring and denoising, it is based on our new model with TV norm to denoise the deblurred image which is penalized by l0 norm of the framelet transform. Our combinational algorithm performs deblurring and denoising alternately. We apply the proximity algorithm to solve the TV regularization for denoising part, and the mean doubly augmented Lagrangian (MDAL) method is used to solve l0 minimization in the analysis-based sparsity for the deblurring part. Experimental results show that the proposed alternating minimization method is robust for a different type of blur and noise. We also do some comparisons with related existing methods to demonstrate that our method has a more significant effect.