A forward-backward view of some primal-dual optimization methods in image recovery
Optimization and Control
2014-06-23 v1
Abstract
A wide array of image recovery problems can be abstracted into the problem of minimizing a sum of composite convex functions in a Hilbert space. To solve such problems, primal-dual proximal approaches have been developed which provide efficient solutions to large-scale optimization problems. The objective of this paper is to show that a number of existing algorithms can be derived from a general form of the forward-backward algorithm applied in a suitable product space. Our approach also allows us to develop useful extensions of existing algorithms by introducing a variable metric. An illustration to image restoration is provided.
Keywords
Cite
@article{arxiv.1406.5439,
title = {A forward-backward view of some primal-dual optimization methods in image recovery},
author = {Patrick L. Combettes and Laurent Condat and Jean-Christophe Pesquet and Bang Cong Vu},
journal= {arXiv preprint arXiv:1406.5439},
year = {2014}
}