  Although many methods have been proposed to deal with nature image
super-resolution (SR) and get impressive performance, the text images SR is not
good due to their ignorance of document images. In this paper, we propose a
matting-based dual generative adversarial network (mdGAN) for document image
SR. Firstly, the input image is decomposed into document text, foreground and
background layers using deep image matting. Then two parallel branches are
constructed to recover text boundary information and color information
respectively. Furthermore, in order to improve the restoration accuracy of
characters in output image, we use the input image's corresponding ground truth
text label as extra supervise information to refine the two-branch networks
during training. Experiments on real text images demonstrate that our method
outperforms several state-of-the-art methods quantitatively and qualitatively.
