点击次数：

论文类型：期刊论文

发表时间：2017-10-01

发表刊物：SIGNAL PROCESSING-IMAGE COMMUNICATION

收录刊物：Scopus、SCIE、EI

卷号：58

页面范围：134-145

ISSN号：0923-5965

关键字：Blind image deblurring; Low rank prior; Salient edges; Kernel estimation; Image restoration

摘要：Blind image deblurring, i.e., estimating a blur kernel from a single blurred, image, is a severely ill-posed problem. In this paper, we find that the blur process changes the similarity of neighboring image patches. Based on the intriguing observation, we show how to effectively apply the low rank prior to blind image deblurring and present a new algorithm that combines low rank prior and salient edge selection. The low rank prior provides data-authentic prior for the intermediate latent image restoration, while salient edges provide reliable edge information for kernel estimation. When estimating blur kernels, salient edges are extracted from an intermediate latent image solved by combining the predicted edges and the low rank prior, which are able to remove tiny details and preserve sharp edges in the intermediate latent image estimation thus facilitating blur kernel estimation. We analyze the effectiveness of the low rank prior in image deblurring and show that it is able to favor clear images over blurred ones. In addition, we show that the proposed method can be extended to non-uniform image deblurring. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art algorithms, both qualitatively and quantitatively. (C) 2017 Elsevier B.V. All rights reserved.