# Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring

arXiv: https://arxiv.org/abs/1902.04062 · HTML: https://mlsanity.com/paper/1902.04062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training](https://mlsanity.com/paper/2102.00267/) (2021)

## Abstract (arXiv)

In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing. We solve this problem by the penalty method and propose the iteratively reweighted alternating minimization algorithm. To speed up the algorithm, we also apply the continuation strategy to the penalty parameter. A convergence result is proved for the algorithm. Compared with the nonconvex ADMM, the proposed algorithm enjoys both theoretical and computational advantages like weaker convergence requirements and faster speed. Numerical results demonstrate the efficiency of the proposed algorithm.