Signal, Image and Video Processing

, Volume 9, Issue 8, pp 1737–1749 | Cite as

Epigraphical projection and proximal tools for solving constrained convex optimization problems

  • G. Chierchia
  • N. Pustelnik
  • J.-C. Pesquet
  • B. Pesquet-Popescu
Invited Paper


We propose a proximal approach to deal with a class of convex variational problems involving nonlinear constraints. A large family of constraints, proven to be effective in the solution of inverse problems, can be expressed as the lower-level set of a sum of convex functions evaluated over different blocks of the linearly transformed signal. For such constraints, the associated projection operator generally does not have a simple form. We circumvent this difficulty by splitting the lower-level set into as many epigraphs as functions involved in the sum. In particular, we focus on constraints involving \(\varvec{\ell }_q\)-norms with \(q\ge 1\), distance functions to a convex set, and \(\varvec{\ell }_{1,p}\)-norms with \(p\in \{2,{+\infty }\}\). The proposed approach is validated in the context of image restoration by making use of constraints based on Non-Local Total Variation. Experiments show that our method leads to significant improvements in term of convergence speed over existing algorithms for solving similar constrained problems.


Iterative methods Optimization Epigraph Projection Proximal algorithms Restoration Total variation Non-local regularization Patch-based processing 


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • G. Chierchia
    • 1
  • N. Pustelnik
    • 2
  • J.-C. Pesquet
    • 3
  • B. Pesquet-Popescu
    • 1
  1. 1.Institut Mines-Télécom, CNRS LTCITélécom ParisTech ParisFrance
  2. 2.Laboratoire de Physique, CNRS-UMR 5672ENS Lyon LyonFrance
  3. 3.LIGM, CNRS-UMR 8049Université Paris-EstMarne-la-ValléeFrance

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