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49. Text Image Deblurring Using Kernel Sparsity Prior

Author

Jianbing Shen, Xianyong Fang, Qiang Zhou, Christian Jacquemin, and Ling Shao

Subjects
Deblurring, Source code, media_common.quotation_subject, Noise reduction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 0502 economics and business, Prior probability, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Image restoration, Mathematics, media_common, 050210 logistics & transportation, Pixel, 05 social sciences, Computer Science Applications, Human-Computer Interaction, Kernel (image processing), Control and Systems Engineering, Computer Science::Computer Vision and Pattern Recognition, Norm (mathematics), 020201 artificial intelligence & image processing, Algorithm, Software, Information Systems
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 ${L} _{\boldsymbol {0}}$ -norm for regularizing the blur kernel in the deblurring model, besides the ${L} _{\boldsymbol {0}}$ sparse priors for the text image and its gradient. Such a ${L} _{\boldsymbol {0}}$ -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 .

Published
2020

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