Awesome-Super-Resolution

"Awesome-Super-Resolution" is a meticulously curated GitHub repository that compiles a vast array of resources pertinent to the field of super-resolution. This collection is an invaluable asset for researchers, developers, and enthusiasts looking to delve into the latest advancements in enhancing image and video quality. The repository systematically organizes academic papers, associated datasets, and links to code repositories, making it easier to access cutting-edge research and implementation details.

The resource prominently features recent developments in super-resolution techniques, including methods like CLIP-SR for collaborative linguistic and image processing, Diffusion Prior Interpolation for real-world face super-resolution, and StructSR for refusing spurious details. It also covers novel approaches such as Generalized and Efficient 2D Gaussian Splatting for arbitrary-scale super-resolution and BF-STVSR for high-fidelity spatial-temporal video super-resolution. Further contributions include efficient attention-sharing transformers, visual autoregressive modeling, and binary one-step diffusion models leveraging flexible matrix compression.

The repository also highlights innovative methods for real-world applications, such as Fast Omni-Directional Image Super-Resolution, Spatial Degradation-Aware and Temporal Consistent Diffusion Models for compressed video, and CondiQuant for low-bit quantization. It extends to specialized areas like infrared image super-resolution with DifIISR, LUT-based image super-resolution with AutoLUT, and quantization via reverse-module and timestep-retraining with QArtSR. The emphasis on providing direct links to arXiv preprints and GitHub codebases makes this repository a crucial starting point for anyone looking to understand, replicate, or build upon existing super-resolution methodologies.