Parallel proximal methods for total variation minimization

Ulugbek S. Kamilov

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arXiv · 1510.00466

Parallel proximal methods for total variation minimization

Ulugbek S. Kamilov

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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.

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Ulugbek S. Kamilov. 2016-01-04. Parallel proximal methods for total variation minimization. https://arxiv.org/abs/1510.00466

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