SwinESRGAN Comparative Analysis

Single image super-resolution (SISR) has experienced great advances through deep learning, particularly with models that balance between perceptual and reconstruction quality. In this work, we offer a comparative assessment of three established super-resolution models—SRGAN, EDSR, and WDSR—and a new hybrid method described in this work, Swin-ESRGAN, by integrating the Swin Transformer structure with the Real-ESRGAN model. We benchmark each model using significant performance characteristics like PSNR, SSIM, inference timing, and visual qualitative results. Our findings report that while the best quantitative outputs are obtained through EDSR, it remains computationally very costly. SRGAN yields fine photo-realistic textures but carries visual artifacts. WDSR possesses a compact and efficient framework with similar performances. Our Swin-ESRGAN offers a great perceptual quality-efficiency tradeoff, offering boosted texture synthesis and structural preservation at the expense of faster training and inference. Trade-offs between realism, accuracy, and efficiency are demonstrated here and have implications on the choice of suitable SR models in practical application.