Text Image Deblurring Using Kernel Sparsity Prior
Xianyong Fang, Qiang Zhou, Jianbing Shen, Christian Jacquemin, Ling Shao · IEEE Transactions on Cybernetics · 2018
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 L0sparse 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.