Multiframe image restoration in the presence of noisy blur kernel

Authors:
Miyoun Jung;Antonio Marquina;Luminita A. Vese
Affiliations:
University of California, Los Angeles, Department of Mathematics, Los Angeles, CA and Universidad de Valencia, Departamento de Matematica Aplicada, Burjassot, Spain;University of California, Los Angeles, Department of Mathematics, Los Angeles, CA and Universidad de Valencia, Departamento de Matematica Aplicada, Burjassot, Spain;University of California, Los Angeles, Department of Mathematics, Los Angeles, CA and Universidad de Valencia, Departamento de Matematica Aplicada, Burjassot, Spain
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

We wish to recover an original image u from several blurry-noisy versions fk, called frames. We assume a more severe degradation model, in which the image u has been blurred by a noisy (stochastic) point spread function. We consider the problem of restoring the degraded image in a variational framework. Since the recovery of u from one single frame f is a highly ill-posed problem, we formulate two minimization problems based on the multiframe approach proposed for image super-resolution by Marquina-Osher [13]. Several experimental results for image restoration are shown, illustrating that the proposed models give visually satisfactory results.