Blind deblurring of text images using a text-specific hybrid dictionary

Abstract

In this dissertation, we propose a blind text image deblurring algorithm by using a text-specific hybrid dictionary. After careful analysis, we find that the text-specific hybrid dictionary has the great ability of providing powerful contextual information for text image deblurring. Here, it is worth noting that our proposed method is inspired by our observation that an intermediate latent image contains not only sharp regions, but also multiple types of small blurred regions. Based upon our discovery, we propose a prior for text images based on sparse representation, which models the relationship between an intermediate latent image and a desired sharp image. To this end, we carefully collect three different image patch pairs, which are 1) Gaussian blur-sharp, 2) motion blur-sharp, and 3) sharp-sharp, in order to construct the text-specific hybrid dictionary. We also propose a new optimization framework suitable for the task of text image deblurring in this dissertation. Extensive experiments have been conducted on a challenging dataset of synthetic and real-world text images. Our results demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods both quantitatively and qualitatively.