swin2sr

Why we included this project

Swin2SR is the official PyTorch implementation of a Swin Transformer V2 model built for low-level vision, and it is a solid reference point if you work with degraded or compressed imagery. It handles three related jobs in one codebase: classical and lightweight image super-resolution, JPEG compression artifact removal, and super-resolution of already-compressed inputs, the last being the scenario where it earned a top solution at the AIM 2022 challenge. Teams with bandwidth-limited or archival image pipelines will find the pretrained models and inference script useful for upscaling low-quality JPEGs by a configurable factor, and the Colab, Hugging Face Spaces, and Kaggle demos let you test before integrating. It is also a practical baseline for researchers comparing transformer-based restoration with convolutional approaches, since it builds directly on SwinIR and documents the training setup. The repository is research-oriented rather than a turnkey service, so expect to run the provided test script and adapt it rather than consume a packaged API.