cszn/BSRGAN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1386 stars · 191 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BSRGAN is a PyTorch implementation of a practical degradation model for deep blind image super-resolution, presented at ICCV 2021. It provides trained models and a degradation pipeline that synthesizes realistic low-quality images for training super-resolution networks.

Use cases

• upscale real-world low-quality photos with deep learning
• generate realistic degraded training data for super-resolution models
• train a blind super-resolution network in PyTorch
• enhance resolution of images with unknown degradations
• apply BSRGAN pretrained models at scale factors 2 and 4

When to choose

• you need to super-resolve real-world images with mixed, unknown degradations
• you want a realistic degradation pipeline to train blind SR models
• you want pretrained PyTorch models with available training code

When to avoid

• you only need classical bicubic/Lanczos upscaling without deep learning
• you need lightweight on-device super-resolution rather than GPU-based research models
• you want a maintained product-grade tool rather than research code

Facets

library · maturity stable

image-processing machine-learning deep-learning computer-vision image-processing deep-learning machine-learning python cross-platform super-resolution blind-super-resolution pytorch degradation-model image-restoration research-code iccv-2021 gpu

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