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

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摘要

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.

论文关键词:Image restoration,Noisy blur kernel,Variational model,Total variation,Nonlocal method,Multiframe model

论文评审过程:Received 2 February 2012, Revised 18 June 2012, Available online 4 August 2012.

论文官网地址:https://doi.org/10.1016/j.cam.2012.07.009