A Survey on Image Super-Resolution with Generative Adversarial Networks

Abstract

Super-resolution is a process to increase image dimensions with a specific upscaling factor while trying to preserve details that matche with the original high-resolution form. Super-resolution can be done with many techniques. But the most effective technique is the one that takes advantage of several neural network designs. Some network designs are more appropriate than others on the specific subject. This study focuses on super resolution studies using Generative Adversarial Network. Many studies use this neural network type to look at various topics such as artificial data production and making the data more meaningful. The key point of this neural network type is having two different sub-networks that try to defeat each other in order to make more realistic results. Performance metrics that measure the quality of a generated image, loss functions used in a neural network and research papers on super-resolution with Generative Adversarial Network are the main domains of this study.