IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution
<|reference_start|>IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution: 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. This paper extends the CFAT model to an improved GAN-based model called IG-CFAT to effectively exploit the performance of transformers in real-world image super-resolution. IG-CFAT incorporates a semantic-aware discriminator to reconstruct fine details more accurately. Moreover, our model utilizes an adaptive degradation model to better simulate real-world degradations. Our methodology adds wavelet loss to conventional loss functions of GAN-based super-resolution models to recover high-frequency details more efficiently. Empirical results demonstrate that IG-CFAT sets new benchmarks in real-world image super-resolution, outperforming SOTA models in quantitative and qualitative metrics.<|reference_end|>
@article{aghelan2024ig-cfat:,
    title={IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting
  Transformers in Real-World Image Super-Resolution},
    author={Alireza Aghelan, Ali Amiryan, Abolfazl Zarghani, Behnoush Hatami,
  Modjtaba Rouhani},
    journal={arXiv preprint arXiv:2406.13815},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13815},
    primaryClass={eess.IV cs.CV}
}