IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution

TL;DR

This paper introduces IG-CFAT, a GAN-based framework that leverages transformer models with novel components like semantic-aware discriminators and wavelet loss to significantly improve real-world image super-resolution performance.

Contribution

It proposes a new GAN framework integrating CFAT transformers, semantic-aware discriminator, adaptive degradation, and wavelet loss for enhanced real-world image super-resolution.

Findings

Outperforms state-of-the-art models in quantitative metrics

Achieves superior qualitative reconstruction of fine details

Demonstrates robustness in real-world scenarios

Abstract

In the field of single image super-resolution (SISR), transformer-based models, have demonstrated significant advancements. However, the potential and efficiency of these models in applied fields such as real-world image super-resolution have been less noticed and there are substantial opportunities for improvement. Recently, composite fusion attention transformer (CFAT), outperformed previous state-of-the-art (SOTA) models in classic image super-resolution. In this paper, we propose a novel GAN-based framework by incorporating the CFAT model to effectively exploit the performance of transformers in real-world image super-resolution. In our proposed approach, we integrate a semantic-aware discriminator to reconstruct fine details more accurately and employ an adaptive degradation model to better simulate real-world degradations. Moreover, we introduce a new combination of loss functions by adding wavelet loss to loss functions of GAN-based models to better recover high-frequency details. Empirical results demonstrate that IG-CFAT significantly outperforms existing SOTA models in both quantitative and qualitative metrics. Our proposed model revolutionizes the field of real-world image super-resolution and demonstrates substantially better performance in recovering fine details and generating realistic textures. The introduction of IG-CFAT offers a robust and adaptable solution for real-world image super-resolution tasks.