swz30/Restormer

Health v2 · maintenance only

Adoption not part of the score

2625 stars · 329 forks observed · 2026-08-28

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

Restormer is an efficient Transformer architecture for high-resolution image restoration, published as a CVPR 2022 Oral paper. It provides PyTorch training and testing code with pre-trained models for motion deblurring, deraining, denoising, and defocus deblurring.

Use cases

• remove motion blur from photos
• denoise images with gaussian or real noise
• remove rain streaks from images
• correct defocus blur in pictures
• restore high-resolution images with a transformer model
• train an image restoration model in pytorch

When to choose

• you need state-of-the-art restoration for deblurring, deraining, or denoising
• you work with high-resolution images where standard vision transformers are too slow
• you want pre-trained models plus training code for low-level vision research

When to avoid

• you need a production-ready image editing application with a polished UI
• your task is high-level vision like detection or segmentation rather than restoration
• you lack a GPU and cannot afford computationally heavy inference

Facets

library · maturity stable

image-processing machine-learning deep-learning computer-vision image-processing deep-learning artificial-intelligence python cross-platform image-restoration image-deblurring image-deraining denoising transformer pytorch low-level-vision cvpr2022 high-resolution research-code gpu

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markdown · JSON · MCP: product_card(name="swz30/Restormer")