Fast and Exact Discrete Image Restoration Based on Total Variation and on Its Extensions to Levelable Potentials

From LRDE

Abstract

We investigate the decomposition property of posterior restoration energies on level sets in a discrete Markov Random Field framework. This leads us to the concept of 'levelable' potentials (which TV is shown to be the paradigm of). We prove that convex levelable posterior energies can be minimized exactly with level-independant binary graph cuts. We extend this scheme to the case of non-convex levelable energies, and present convincing restoration results for images degraded by impulsive noise.


Bibtex (lrde.bib)

@InProceedings{	  darbon.06.siam,
  author	= {J\'er\^ome Darbon and Marc Sigelle},
  title		= {Fast and Exact Discrete Image Restoration Based on Total
		  Variation and on Its Extensions to Levelable Potentials},
  booktitle	= {SIAM Conference on Imaging Sciences},
  year		= 2006,
  address	= {Minneapolis, USA},
  month		= may,
  abstract	= {We investigate the decomposition property of posterior
		  restoration energies on level sets in a discrete Markov
		  Random Field framework. This leads us to the concept of
		  'levelable' potentials (which TV is shown to be the
		  paradigm of). We prove that convex levelable posterior
		  energies can be minimized exactly with level-independant
		  binary graph cuts. We extend this scheme to the case of
		  non-convex levelable energies, and present convincing
		  restoration results for images degraded by impulsive
		  noise.}
}