caiyuanhao1998/MST

• highreadme#1

  Reposition README H1 to explicitly clarify the project's domain

  原因：

  当前

  # A Toolbox for Spectral Compressive Imaging

  复制粘贴的修复

  复制

  # `caiyuanhao1998/MST`: A Toolbox for Spectral Compressive Imaging Reconstruction (NOT Minimum Spanning Tree)

  待办完成忽略
• mediumhomepage#2

  Add a homepage URL to the repository metadata

  原因：

  复制粘贴的修复

  复制

  https://[your-project-homepage-url]

  待办完成忽略
• mediumreadme#3

  Expand README introduction with a clear value proposition and algorithm list

  原因：

  当前

  #### Introduction
  This is a baseline and toolbox for spectral compressive imaging reconstruction. This repo supports **over 15** algorithms. Our method MST++ won the NTIRE 2022 Challenge on spectral recovery from RGB images. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.

  复制粘贴的修复

  复制

  #### Introduction
  This repository provides a comprehensive baseline and toolbox for **Spectral Compressive Imaging (SCI) reconstruction**. It supports **over 15 state-of-the-art algorithms**, including MST (Mask-guided Spectral-wise Transformer), CST, DAUHST, BiSCI, HDNet, and MST++. Our methods have achieved top results, notably MST++ winning the NTIRE 2022 Challenge on spectral recovery from RGB images. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What tools are available for efficient hyperspectral image reconstruction from compressed data?

  你：未被推荐
  AI 推荐顺序：

  1. TensorFlow/Keras
  2. PyTorch
  3. MATLAB
  4. scikit-learn
  5. OpenCV
  6. HyperSpy

  AI 推荐了 6 个替代方案，却始终没点名 caiyuanhao1998/MST。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking a library for spectral image restoration using transformer models or binarized networks.

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers (huggingface/transformers)
  2. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
  3. TensorFlow Models (Official Models) (tensorflow/models)
  4. MMEditing (OpenMMLab) (open-mmlab/mmediting)
  5. Brevitas (Xilinx/brevitas)
  6. KerasCV (keras-team/keras-cv)

  AI 推荐了 6 个替代方案，却始终没点名 caiyuanhao1998/MST。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of caiyuanhao1998/MST?

  pass
  ⚠AI 未点名 caiyuanhao1998/MST —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts caiyuanhao1998/MST in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 caiyuanhao1998/MST

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo caiyuanhao1998/MST solve, and who is the primary audience?

  pass
  ⚠AI 未点名 caiyuanhao1998/MST —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？