Non-local regularization of inverse problems

Gabriel Peyré; Sébastien Bougleux; Laurent Cohen

doi:10.3934/ipi.2011.5.511

Article Contents

2011, Volume 5, Issue 2: 511-530. Doi: 10.3934/ipi.2011.5.511

This issue Previous Article Recovering conductivity at the boundary in three-dimensional electrical impedance tomography Next Article

Non-local regularization of inverse problems

1.
Ceremade, Université Paris-Dauphine, 75775 Paris Cedex 16
2.
GREYC, Université de Caen, 14050 Caen Cedex

Received: October 2009

Revised: September 2010

Published: May 2011

Abstract / Introduction Related Papers Cited by

Abstract

This article proposes a new framework to regularize imaging linear inverse problems using an adaptive non-local energy. A non-local graph is optimized to match the structures of the image to recover. This allows a better reconstruction of geometric edges and textures present in natural images. A fast algorithm computes iteratively both the solution of the regularization process and the non-local graph adapted to this solution. The graph adaptation is efficient to solve inverse problems with randomized measurements such as inpainting random pixels or compressive sensing recovery. Our non-local regularization gives state-of-the-art results for this class of inverse problems. On more challenging problems such as image super-resolution, our method gives results comparable to sparse regularization in a translation invariant wavelet frame.

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Mathematics Subject Classification: Primary: 68U10, 94A08; Secondary: 49N45.

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