Evaluation of Deep Super Resolution Methods for Textual Images

摘要

Super-resolution (SR) is one of the important pre-processing methods to refine the text images quality. Though there are numerous introduced algorithms to increase the spatial resolution for textual images, analysis on SR methods using deep learning is still insufficient. In this paper, we focus on evaluating the performance of various deep SR methods which have already confirmed to perform well in natural images super-resolution. Three evaluation metrics are used to analyze the performance of each method, such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and optical character recognition accuracy (OCRAcc). Experimental results show that deeper networks perform better than shallow networks for super-resolution problem. In overall, deep recursive convolutional network (DRCN) and deep laplacian pyramid network (LapSRN) alternately achieve the best performance. Then, very deep super-resolution network (VDSR) obtains the 3rdrank following both methods.