Super-resolution image by using semantics to reconstruct details with IG-CFAT
Super-resolution image by using semantics to reconstruct details with IG-CFAT
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution
arXiv paper abstract https://arxiv.org/abs/2406.13815
arXiv PDF paper https://arxiv.org/pdf/2406.13815
In the field of single image super-resolution (SISR), transformer-based models, have demonstrated significant advancements.
... 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 ... details ... accurately, ... improving ... quality ... utilizes an adaptive degradation ... to ... simulate ... degradations.
... methodology adds wavelet losses to conventional loss functions of GAN-based super-resolution models to reconstruct high-frequency details more efficiently.
... IG-CFAT sets new benchmarks in real-world image super-resolution, outperforming SOTA models in both quantitative and qualitative metrics.
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