Generative Image Super Resolution

Generative image super-resolution (SR) aims to enhance the resolution of low-resolution images using generative models, primarily focusing on improving perceptual quality and minimizing artifacts. Current research emphasizes refining existing generative adversarial networks (GANs) and diffusion models, often incorporating techniques like wavelet-domain losses, self-similarity constraints, and semantic awareness to better control artifact generation and preserve image details. These advancements are significant for various applications, including improving the quality of climate data, enhancing robotic vision systems, and generally improving image processing capabilities across diverse fields.