Journal of Mathematical Imaging and Vision

, Volume 40, Issue 1, pp 120–145

A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging

Article

DOI: 10.1007/s10851-010-0251-1

Cite this article as:
Chambolle, A. & Pock, T. J Math Imaging Vis (2011) 40: 120. doi:10.1007/s10851-010-0251-1

Abstract

In this paper we study a first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure. We prove convergence to a saddle-point with rate O(1/N) in finite dimensions for the complete class of problems. We further show accelerations of the proposed algorithm to yield improved rates on problems with some degree of smoothness. In particular we show that we can achieve O(1/N2) convergence on problems, where the primal or the dual objective is uniformly convex, and we can show linear convergence, i.e. O(ωN) for some ω∈(0,1), on smooth problems. The wide applicability of the proposed algorithm is demonstrated on several imaging problems such as image denoising, image deconvolution, image inpainting, motion estimation and multi-label image segmentation.

Keywords

Convex optimizationDual approachesTotal variationInverse problemsImageReconstruction

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.CMAP, Ecole PolytechniqueCNRSPalaiseauFrance
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria