Modeling the distribution of patches with shift-invariance: application to SAR image restoration

Sonia Tabti (1) , Charles-Alban Deledalle (2) , Loïc Denis (3) , Florence Tupin (4, 5)

1 TSI-TII radar
2 IMB - Institut de Mathématiques de Bordeaux
3 LHC - Laboratoire Hubert Curien
4 LTCI - Laboratoire Traitement et Communication de l'Information
5 Télécom ParisTech

Patches have proven to be very effective features to model natural images and to design image restoration methods. Given the huge diversity of patches found in images, modeling the distribution of patches is a difficult task. Rather than attempting to accurately model all patches of the image, we advocate that it is sufficient that all pixels of the image belong to at least one well-explained patch. An image is thus described as a tiling of patches that have large prior probability. In contrast to most patch-based approaches, we do not process the image in patch space, and consider instead that patches should match well everywhere where they overlap. In-order to apply this modeling to the restoration of SAR images, we define a suitable data-fitting term to account for the statistical distribution of speckle. Restoration results are competitive with state-of-the art SAR despeckling methods.

Mots clés: SAR images; gaussian mixture models; patches; despeckling

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Sonia Tabti, Charles-Alban Deledalle, Loïc Denis, Florence Tupin. Modeling the distribution of patches with shift-invariance: application to SAR image restoration. 2014. ⟨hal-01006733⟩