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Fast Blind Deblurring via Normalized Sparsity Prior

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Indexed by:Journal Papers

Date of Publication:2013-11-01

Journal:Journal of Information and Computational Science

Included Journals:EI、Scopus

Volume:10

Issue:16

Page Number:5083-5091

ISSN No.:15487741

Abstract:Image deblurring is a longstanding problem in digital image processing and computer vision community. In this paper, we propose a new single image blind deconvolution method. For kernel estimation, salient edges are extracted from blurred image to increase the robustness of kernel estimation. To preserve the natural properties of latent images, we employ a powerful natural image prior to guide the latent image restoration. An efficient and fast optimization method is proposed to solve our proposed model. We have extensively tested our algorithm, and found that is able to provide correct kernel estimates and sharp images.

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