Text image deblurring via two-tone prior

General natural image deblurring methods do not work well for document images. We exploit a two-tone prior to steer the intermediate latent image towards a piece-wise constant image with only two distinct gray levels. This prior is helpful for the process of kernel estimation to overcome undesirable local minima, and it is not too restrictive to deblur text images with complex backgrounds. Our kernel estimation method comprises two stages, where we first employ contrast-enhancing two-tone prior and then use intermediate-value inhibition regularizer. The resulting optimization formulation is solved by half-quadratic splitting and alternating minimization techniques. The experimental results show that the proposed method is capable of achieving accurate results and compares well with the state-of-the-art.