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Rapport (Rapport Technique) Année : 2017

A convex approach to super-resolution and regularization of lines in images

Résumé

We present a new convex formulation for the problem of recovering lines in degraded images. Following the recent paradigm of super-resolution, we formulate a dedicated atomic norm penalty and we solve this optimization problem by means of a primal–dual algorithm. This parsimonious model enables the reconstruction of lines from lowpass measurements, even in presence of a large amount of noise or blur. Furthermore, a Prony method performed on rows and columns of the restored image, provides a spectral estimation of the line parameters, with subpixel accuracy.
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Dates et versions

hal-01599010 , version 1 (30-09-2017)
hal-01599010 , version 2 (27-04-2018)
hal-01599010 , version 3 (10-10-2018)
hal-01599010 , version 4 (19-11-2018)

Identifiants

  • HAL Id : hal-01599010 , version 2

Citer

Kévin Polisano, Laurent Condat, Marianne Clausel, Valérie Perrier. A convex approach to super-resolution and regularization of lines in images. [Technical Report] Laboratoire Jean Kuntzmann (LJK). 2017. ⟨hal-01599010v2⟩

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