Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels

  • Authors:

  • Miyoun Jung;Antonio Marquina;Luminita A. Vese

  • Affiliations:

  • CEREMADE, Université de Paris-Dauphine, Paris 75775, France;Departamento de Matematica Aplicada, Universidad de Valencia, Burjassot, 46100, Spain;Department of Mathematics, University of California, Los Angeles, CA 90095, USA

  • Venue:
  • Journal of Computational and Applied Mathematics
  • Year:
  • 2013

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Abstract

This article introduces and explores a class of degradation models in which an image is blurred by a noisy (stochastic) point spread function (PSF). The aim is to restore a sharper and cleaner image from the degraded one. Due to the highly ill-posed nature of the problem, we propose to recover the image given a sequence of several observed degraded images or multiframes. Thus we adopt the idea of the multiframe approach introduced for image super-resolution, which reduces distortions appearing in the degraded images. Moreover, we formulate variational minimization problems with the robust (local or nonlocal) L^1 edge-preserving regularizing energy functionals, unlike prior works dealing with stochastic point spread functions. Several experimental results on grey-scale/color images and on real static video data are shown, illustrating that the proposed methods produce satisfactory results. We also apply the degradation model to a segmentation problem with simultaneous image restoration.