Single-image super-resolution reconstruction via generative adversarial network

Abstract

Single-image super-resolution (SISR) reconstruction is important for image processing, and lots of algorithms based on deep convolutional neural network (CNN) have been proposed in recent years. Although these algorithms have better accuracy and recovery results than traditional methods without CNN, they ignore finer texture details when super-resolving at a large upscaling factor. To solve this problem, in this paper we propose an algorithm based on generative adversarial network for single-image super-resolution restoration at 4x upscaling factors. We decode a restored high-resolution image by the generative network and make the generator output results finer, more realistic texture details by the adversarial network. We performed experiments on the DIV2K dataset and proved that our method has better performance in single image super-resolution reconstruction. The image quality of this reconstruction method is improved at the peak signal-tonoise ratio and structural similarity index and the results have a good visual effect.