Convolutional neural networks have recently demonstrated interesting results for single image super-resolution. However, these networks were trained to deal with super-resolution problem on natural images. In this paper, we adapt a deep network, which was proposed for natural images superresolution, to single text image super-resolution. To evaluate the network, we present our database for single text image super-resolution. Moreover, we propose to combine Gradient Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution image. Quantitative and qualitative evaluations on our dataset show that adding the GDL improves the super-resolution results.
@article{arxiv.1811.10449,
title = {Deep Laplacian Pyramid Network for Text Images Super-Resolution},
author = {Hanh T. M. Tran and Tien Ho-Phuoc},
journal= {arXiv preprint arXiv:1811.10449},
year = {2018}
}