Text image deblurring using kernel sparsity prior

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Fang, Xianyong, Zhou, Qiang, Shen, Jianbing, Jacquemin, Christian and Shao, Ling (2020) Text image deblurring using kernel sparsity prior. IEEE Transactions on Cybernetics, 50 (3). pp. 997-1008. ISSN 2168-2267

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Abstract

Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the L₀-norm for regularizing the blur kernel in the deblurring model, besides the L₀ sparse priors for the text image and its gradient. Such a L₀-norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at: https://github.com/shenjianbing/text-image-deblur.

Item Type: Article
Additional Information: Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61502005, in part by the Anhui Science Foundation under Grant 1608085QF129, in part by the Key Programs for Science and Technology Development of Anhui Province under Grant 1604d0802004, and in part by the Beijing Natural Science Foundation under Grant 4182056.
Uncontrolled Keywords: l₀-norm,motion deblurring,text image,l-0-norm,video,regularization,software,information systems,human-computer interaction,electrical and electronic engineering,control and systems engineering,computer science applications ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 19 Nov 2018 10:30
Last Modified: 22 Oct 2022 04:17
URI: https://ueaeprints.uea.ac.uk/id/eprint/68939
DOI: 10.1109/TCYB.2018.2876511

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