# JingyunLiang/SwinIR

SwinIR: Image Restoration Using Swin Transformer (official repository)

Repository: https://github.com/JingyunLiang/SwinIR
Canonical: https://ross.abutalabs.com/products/swinir
Homepage: https://arxiv.org/abs/2108.10257
Language: Python
License: Apache-2.0
License Family: permissive
Topics: image-super-resolution, image-denoising, compression-artifact-reduction, image-deblocking, transformer, real-world-image-super-resolution, lightweight-image-super-resolution, image-restoration, low-level-vision, vision-transformer, image-sr, restoration, super-resolution, denoising, deblocking, decompression
Last push: 2024-05-14T07:05:48+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1843, "days_push": 841, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5580, forks 665 (observed 2026-08-28T04:09:22.552919+00:00)

## What it is
Official PyTorch implementation of SwinIR, a Swin Transformer-based model for image restoration tasks including super-resolution, denoising, and JPEG compression artifact reduction. It provides pretrained models, training/evaluation code, and demos (Colab, Gradio, Replicate, mobile) achieving state-of-the-art results on low-level vision benchmarks.

## Use cases
- upscale low-resolution images to high resolution
- remove noise from grayscale or color photos
- reduce JPEG compression artifacts in images
- restore real-world degraded photos with super-resolution
- run lightweight super-resolution on mobile devices
- deblock compressed images before further processing

## When to choose
- you need state-of-the-art image super-resolution or denoising with pretrained models
- you want a transformer-based restoration baseline for research or comparison
- you need to clean up JPEG-compressed or noisy images in a Python pipeline
- you want a lightweight SR model that runs on mobile or via web demos

## When to avoid
- you need video restoration rather than single-image processing
- you require real-time inference on CPU-constrained hardware
- you want a general-purpose image editor rather than a restoration model
- your task is high-level vision like detection or segmentation

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, computer-vision, deep-learning, machine-learning
- domain: computer-vision, image-processing, deep-learning, machine-learning
- platform: python, cross-platform
- tags: super-resolution, image-denoising, jpeg-artifact-reduction, image-deblocking, swin-transformer, vision-transformer, pytorch, low-level-vision, pretrained-models, image-restoration, gpu

## Member repositories
- JingyunLiang/SwinIR (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.552919+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:33.615152+00:00, confidence not recorded.
  - readme: https://github.com/JingyunLiang/SwinIR (fetched 2026-08-28T04:09:22.552919+00:00, sha bf05e129f278)
  - homepage: https://arxiv.org/abs/2108.10257 (fetched 2026-08-29T08:50:34.495385+00:00, sha 81c9f003aee2)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T08:50:34.502135+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T08:50:34.498126+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T08:50:34.534826+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T08:50:34.500239+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.