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Although many methods have been proposed to deal with nature imagesuper-resolution (SR) and get impressive performance, the text images SR is notgood due to their ignorance of document images. In this paper, we propose amatting-based dual generative adversarial network (mdGAN) for document imageSR. Firstly, the input image is decomposed into document text, foreground andbackground layers using deep image matting. Then two parallel branches areconstructed to recover text boundary information and color informationrespectively. Furthermore, in order to improve the restoration accuracy ofcharacters in output image, we use the input image's corresponding ground truthtext label as extra supervise information to refine the two-branch networksduring training. Experiments on real text images demonstrate that our methodoutperforms several state-of-the-art methods quantitatively and qualitatively.

Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision