Generative Adversarial Networkbased Image SuperResolution with a Novel Quality Loss Single Image SuperResolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and Generative Adversarial Networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with Human Vision System (HVS), we design a quality loss by integrating an IQA metric named Gradient Magnitude Similarity Deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variation of GANs named WGANGP. Experiments show that GMGAN with quality loss and WGANGP can generate visually appealing results and set a new stateofart.

Improving Text Image Resolution using a Deep Generative Adversarial Network for Optical Character Recognition Optical character recognition (OCR) has been widely studied in previous work. Except for the models used, the recognition accuracy depends most on the resolution of the image to be recognized. To enhance OCR performance, this paper proposes an approach based on a generative adversarial network to improve text image resolution. Our approach uses a perceptual loss function that consists of an adversarial loss, a content loss and an L1 loss. The adversarial loss and the L1 loss are used to ensure the generated superresolved images are closer to the ground truth highresolution images. Meanwhile, the content loss is used to ensure the generated superresolved images and the input lowresolution images have similar features on the basis of perceptual instead of pixel similarity. To evaluate the proposed approach, we compare the recognition accuracies before and after improving the resolution of both English and Chinese text images. The results show that the recognition accuracies on the superresolved text images obtained with our approach are significantly higher than those on the lowresolution images without processing.