I developed a cutting-edge super-resolution model to enhance low-resolution images using the DIV2K dataset. By exploring various deep learning approaches, including CNNs, GANs, ResNets, Transformers, Diffusion Models, and Autoencoders, I achieved high PSNR and SSIM scores while retaining intricate details and textures of the original images. The model was validated with comprehensive evaluation metrics and demonstrated superior effectiveness compared to traditional methods, showcasing significant innovation and originality in achieving high-quality super-resolution results.
code and results will publish soon.