Accelerated Proximal Iterative re-Weighted $\ell_1$ Alternating Minimization for Image Deblurring
Abstract
The quadratic penalty alternating minimization (AM) method is widely used for solving the convex total variation (TV) image deblurring problem. However, quadratic penalty AM for solving the nonconvex nonsmooth , TV image deblurring problems is less studied. In this paper, we propose two algorithms, namely proximal iterative re-weighted AM (PIRL1-AM) and its accelerated version, accelerated proximal iterative re-weighted AM (APIRL1-AM) for solving the nonconvex nonsmooth TV image deblurring problem. The proposed algorithms are derived from the proximal iterative re-weighted (IRL1) algorithm and the proximal gradient algorithm. Numerical results show that PIRL1-AM is effective in retaining sharp edges in image deblurring while APIRL1-AM can further provide convergence speed up in terms of the number of algorithm iterations and computational time.
Cite
@article{arxiv.2309.05204,
title = {Accelerated Proximal Iterative re-Weighted $\ell_1$ Alternating Minimization for Image Deblurring},
author = {Tarmizi Adam and Alexander Malyshev and Mohd Fikree Hassan and Nur Syarafina Mohamed and Md Sah Hj Salam},
journal= {arXiv preprint arXiv:2309.05204},
year = {2023}
}