# Parallel proximal methods for total variation minimization

arXiv: https://arxiv.org/abs/1510.00466 · HTML: https://mlsanity.com/paper/1510.00466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2015.

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

- [Mixed X-Ray Image Separation for Artworks with Concealed Designs](https://mlsanity.com/paper/2201.09167/) (2022)

## Abstract (arXiv)

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.