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

• Autores: Miyoun Jung, Antonio Marquina Vila, Luminita A. Vese
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 240, Nº 1, 2013, págs. 123-134
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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) L1 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.