Parallel proximal methods for total variation minimization
Information Theory
2016-01-05 v2 math.IT
Optimization and Control
Abstract
Total variation (TV) is a widely used regularizer for stabilizing the solution of ill-posed inverse problems. In this paper, we propose a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functional. Our method replaces the standard proximal step of TV by a simpler alternative that computes several independent proximals. We prove that the proposed parallel proximal method converges to the TV solution, while requiring no sub-iterations. The results in this paper could enhance the applicability of TV for solving very large scale imaging inverse problems.
Keywords
Cite
@article{arxiv.1510.00466,
title = {Parallel proximal methods for total variation minimization},
author = {Ulugbek S. Kamilov},
journal= {arXiv preprint arXiv:1510.00466},
year = {2016}
}
Comments
To be presented at ICASSP 2016