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.

中文翻译：

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使用内核稀疏先验进行文本图像去模糊


以往的文本图像运动去模糊方法很少考虑模糊核的稀疏特性。本文通过利用文本图像本身和内核的稀疏特性，提出了一种新的文本图像运动去模糊方法。除了文本图像及其梯度的 L0 稀疏先验之外，它还结合了用于在去模糊模型中正则化模糊内核的 L0 范数。这种基于 L0 范数的模型可以通过半二次分裂与快速共轭下降法相结合进行有效优化。为了进一步提高恢复核的质量，还开发了一种保留结构的核去噪方法来滤除噪声像素，产生干净的核曲线。实验结果表明了该方法的优越性。源代码和结果可在：https://github.com/shenjianbing/text-image-deblur 获取。