caiyuanhao1998/MST

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1118 stars · 89 forks observed · 2026-08-28

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

A Python toolbox for spectral compressive imaging reconstruction that implements over 15 algorithms including MST, CST, DAUHST, BiSCI, HDNet, and MST++. It serves as a research baseline with pre-trained models, training logs, and evaluation for hyperspectral image reconstruction.

Use cases

• reconstruct hyperspectral images from compressive measurements
• reproduce CVPR and NeurIPS spectral imaging papers
• benchmark spectral reconstruction algorithms
• recover spectral images from RGB
• train deep learning models for snapshot compressive imaging
• compare transformer-based image restoration methods

When to choose

• you need state-of-the-art spectral compressive imaging baselines
• you want pre-trained models and training code for hyperspectral reconstruction
• you are doing research on snapshot compressive imaging or spectral recovery

When to avoid

• you need a production-ready image processing pipeline rather than research code
• your task is general RGB image restoration unrelated to spectral imaging
• you need a GUI tool rather than a Python research codebase

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python hyperspectral-imaging snapshot-compressive-imaging spectral-reconstruction image-restoration transformer research-code pytorch benchmark linux gpu

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markdown · JSON · MCP: product_card(name="caiyuanhao1998/MST")