A review on Single Image Super Resolution techniques using generative adversarial network

Abstract

Single Image Super Resolution (SISR) is a process to obtain a high pixel density and refined details from a low resolution (LR) image to get upscaled and sharper high-resolution (HR) image. In last decade, SISR based on Convolutional Neural Networks (CNN) have achieved impressive results for generating super-resolved images up to the size of ×3. This technique focuses on minimizing L1/L2 loss between real HR image and generated HR image without considering the perceptual quality of images. To improve upon, SISR based on Generative Adversarial Network (GAN) has gained researchers attention for generating visually pleasing images with reasonably minimizing L1/L2 loss for ×4 size images. The basic idea of GAN is to train two networks simultaneously, a Generator and a Discriminator such that generator can produce the super-resolved image for a given input LR image by learning real HR image distribution. This paper presents an overview of GAN based SISR techniques for further research as there are a few surveys in this area. Different GAN models have been classified in terms of architecture, algorithms, and loss functions including their benefits and limitations. Lastly, the research gaps as well as some possible solutions for the existing methods have been discussed.