csguoh/MambaIR

Health v2 · maintenance only

Adoption not part of the score

1171 stars · 96 forks observed · 2026-08-28

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

MambaIR and MambaIRv2 are PyTorch-based image restoration models built on Mamba state-space models, published at ECCV 2024 and CVPR 2025. They provide attentive state-space backbones for tasks like super-resolution, denoising, and deblurring with pretrained weights on HuggingFace.

Use cases

• restore degraded images with a mamba-based model
• perform image super-resolution
• denoise photos with a state space model
• deblur images using deep learning
• reproduce ECCV/CVPR image restoration baselines
• compare restoration models on PSNR benchmarks

When to choose

• you need efficient global-context image restoration models
• you want state-of-the-art super-resolution or denoising baselines
• you are researching state-space models for low-level vision

When to avoid

• you need a production-ready image editing application
• you work outside image restoration tasks
• you lack a GPU for training or inference

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision image-processing deep-learning artificial-intelligence python image-restoration state-space-models mamba super-resolution denoising deblurring eccv2024 cvpr2025 research-code gpu

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markdown · JSON · MCP: product_card(name="csguoh/MambaIR")