$L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond
We propose a simple yet effective <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq2-2551244.gif"/></alternatives></inline-formula>-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is based on distinctive properties of text images, with which we develop an efficient optimization algorithm to generate reliable intermediate results for kernel estimation. The proposed algorithm does not require any heuristic edge selection methods, which are critical to the state-of-the-art edge-based deblurring methods. We discuss the relationship with other edge-based deblurring methods and present how to select salient edges more principally. For the final latent image restoration step, we present an effective method to remove artifacts for better deblurred results. We show the proposed algorithm can be extended to deblur natural images with complex scenes and low illumination, as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art image deblurring methods.