Generative Adversarial Networks for Image Super-Resolution: A Survey

TL;DR

This survey reviews the development and application of generative adversarial networks (GANs) in single image super-resolution, analyzing various architectures, training methods, and performance outcomes to guide future research.

Contribution

It provides a comprehensive comparison of GAN-based methods for image super-resolution, highlighting their differences, challenges, and potential research directions.

Findings

GANs achieve excellent results in SISR.

Different GAN architectures and training strategies impact performance.

Challenges include stability and quality of generated images.

Abstract

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We begin by surveying the development of GANs and popular GAN variants for image-related applications, and then analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Secondly, we compare the performances of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.