Semantic Super-Resolution of Text Images via Self-Distillation

Abstract

This research develops an effective single-image super-resolution (SR) method that increases the resolution of scanned text or document images and improves their readability. To this end, we introduce a new semantic loss and propose a semantic SR method that guides an SR network to learn implicit text-specific semantic priors through self-distillation. Experiments on the enhanced deep SR (EDSR) model, one of the most popular SR networks, confirmed that semantic loss can contribute to further improving the quality of text SR images. Although the improvement varied depending on image resolution and dataset, the peak signal-to-noise ratio (PSNR) value was increased by up to 0.3 dB by introducing the semantic loss. The proposed method outperformed an existing semantic SR method.