MULTI-IMAGE BLIND DECONVOLUTION USING LOW-RANK REPRESENTATION

Abstract

Blind deconvolution is a restoration process of an image which is blurred by an unknown point spread function (PSF) (a.k.a. blur kernel). Some previous works [1, 2, 3, 4] have shown that multiple blurred captures of the same scene can improve the quality of blind deconvolution result. However, the previous multi-image blind deconvolution methods are prone to inconsistencies between observations such as moving objects, illumination change or mis-alignment. In this paper, we present a new multi-image blind deconvolution algorithm using low-rank representation which utilizes similar components of the multiple images for collaboration. Our framework alternatively solves a Schatten-0 norm low-rank approximation and a MAP-based L-0 norm blind deconvolution for finding the true latent images and their corresponding PSFs and warping parameters. The experimental results show that our approach can recover high quality images in the presence of possible corruptions on both static and moving scenes and outperforms the state-of-the-art results.