State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications

TL;DR

This paper reviews recent transformer-based methods for image super-resolution, highlighting their ability to surpass previous CNN and GAN approaches by capturing global context and high-frequency details, and discusses future research directions.

Contribution

It provides a comprehensive review and critical analysis of recent transformer architectures in image super-resolution, identifying gaps and potential for further advancements.

Findings

Transformer models outperform CNN and GAN in SR quality

Recent techniques effectively capture global and local image features

Identifies unexplored areas for future research in transformer-based SR

Abstract

Image Super-Resolution (SR) aims to recover a high-resolution image from its low-resolution counterpart, which has been affected by a specific degradation process. This is achieved by enhancing detail and visual quality. Recent advancements in transformer-based methods have remolded image super-resolution by enabling high-quality reconstructions surpassing previous deep-learning approaches like CNN and GAN-based. This effectively addresses the limitations of previous methods, such as limited receptive fields, poor global context capture, and challenges in high-frequency detail recovery. Additionally, the paper reviews recent trends and advancements in transformer-based SR models, exploring various innovative techniques and architectures that combine transformers with traditional networks to balance global and local contexts. These neoteric methods are critically analyzed, revealing promising yet unexplored gaps and potential directions for future research. Several visualizations of models and techniques are included to foster a holistic understanding of recent trends. This work seeks to offer a structured roadmap for researchers at the forefront of deep learning, specifically exploring the impact of transformers on super-resolution techniques.