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论文类型：期刊论文

发表时间：2013-10-01

发表刊物：SIGNAL PROCESSING-IMAGE COMMUNICATION

收录刊物：SCIE、EI、Scopus

卷号：28

期号：9

页面范围：1156-1170

ISSN号：0923-5965

关键字：Motion deblurring; Kernel estimation; Image restoration; Salient structures/edges

摘要：Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples. (c) 2013 Elsevier B.V. All rights reserved.