Proximal Splitting Methods in Signal Processing

Abstract

The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. These proximal splitting methods are shown to capture and extend several well-known algorithms in a unifying framework. Applications of proximal methods in signal recovery and synthesis are discussed.

References in corpus (3)

• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• A proximal iteration for deconvolving Poisson noisy images using sparse representations
• Deconvolution of Poissonian Images Using Variable Splitting and Augmented Lagrangian Optimization

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