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Text Image Deblurring Using Kernel Sparsity Prior
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DOI：10.1109/TCYB.2018.2876511.png)
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Abstract
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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-0-norm for regularizing the blur kernel in the deblurring model, besides the L-0 sparse priors for the text image and its gradient. Such a L-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.
Keywords:
L-0-norm
motion deblurring
text image
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