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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.
| Date | Stars |
|---|---|
| 2026-07-30 | 1117 |
| 2026-08-07 | 1119 |
| 2026-08-24 | 1118 |
| 2026-09-01 | 1119 |
| 2026-09-03 | 1120 |
| 2026-09-08 | 1119 |
| 2026-09-18 | 1120 |
| 2026-09-28 | 1121 |
| 2026-10-04 | 1120 |
| 2026-10-09 | 1121 |
| 2026-10-10 | 1119 |
| 2026-10-14 | 1118 |
| 2026-10-18 | 1117 |
| 2026-11-02 | 1120 |
| 2026-11-04 | 1120 |
Today
— stars today
This week
+3 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.27%/day
# A Toolbox for Spectral Compressive Imaging [](https://github.com/caiyuanhao1998/MST-plus-plus/) [](https://zhuanlan.zhihu.com/p/501101943) [](https://zhuanlan.zhihu.com/p/544979161) [](https://zhuanlan.zhihu.com/p/576280023) #### Authors Yuanhao Cai*, Jing Lin*, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van Gool #### Papers - [Binarized Spectral Compressive Imaging (NeurIPS 2023)](https://arxiv.org/abs/2305.10299) - [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (CVPR 2022)](https://arxiv.org/abs/2111.07910) - [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction (ECCV 2022)](https://arxiv.org/abs/2203.04845) - [Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging (NeurIPS 2022)](https://arxiv.org/abs/2205.10102) - [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)](https://arxiv.org/abs/2111.07910) - [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging (CVPR 2022)](https://arxiv.org/abs/2203.02149) #### Awards <img src="./figure/ntire.png" height=240> <img src="./figure/NTIRE_2024.png" height=240> #### Introduction This is a baseline and toolbox for spectral compressive imaging reconstruction. This repo supports **over 15** algorithms. Our method [MST++](https://github.com/caiyuanhao1998/MST-plus-plus) 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. #### News - **2025.02.10 :** [NTIRE 2025 Low-light Image Enhancement Challenge](https://codalab.lisn.upsaclay.fr/competitions/21636) has started. Welcome to use our [Retinexformer](https://github.com/caiyuanhao1998/Retinexformer) and MST to participate in this challenge. 😄 - **2024.04.09 :** We release the results of the three traditional model-based methods, i.e., [TwIST](https://ieeexplore.ieee.org/document/4358846), [GAP-TV](https://arxiv.org/pdf/1511.03890.pdf), and [DeSCI](https://arxiv.org/pdf/1807.07837.pdf) for your convenience to conduct research. Feel free to use them. 😄 - **2024.03.21 :** Our methods [Retinexformer](https://github.com/caiyuanhao1998/Retinexformer) and [MST++](https://github.com/caiyuanhao1998/MST-plus-plus) (NTIRE 2022 Spectral Reconstruction Challenge Winner) ranked top-2 in the [NTIRE 2024 Challenge on Low Light Enhancement](https://codalab.lisn.upsaclay.fr/competitions/17640). Code, pre-trained models, training logs, and enhancement results will be released in [the repo of Retinexformer](https://github.com/caiyuanhao1998/Retinexformer). Stay tuned! 🚀 - **2024.02.15 :** [NTIRE 2024 Challenge on Low Light Enhancement](https://codalab.lisn.upsaclay.fr/competitions/17640) begins. Welcome to use our [Retinexformer](https://github.com/caiyuanhao1998/Retinexformer) or [MST++](https://github.com/caiyuanhao1998/MST-plus-plus) (NTIRE 2022 Spectral Reconstruction Challenge Winner) to participate in this challenge! :trophy: - **2023.12.02 :** Codes for real experiments have been updated. Welcome to check and use them. 🥳 - **2023.11.24 :** Code, models, and results of [BiSRNet](https://arxiv.org/pdf/2305.10299.pdf) (NeurIPS 2023) are released at this repo. We also develop a toolbox [BiSCI](https://github.com/caiyuanhao1998/BiSCI) for binarized SCI reconstruction. Feel free to check and use them. 🌟 - **2023.11.02 :** MST, MST++, CST, and DAUHST are added to the [Awesome-Transformer-Attention](https://github.com/cmhungsteve/Awesome-Transformer-Attention/blob/main/README_2.md#image-restoration) collection. 💫 - **2023.09.21 :** Our new work [BiSRNet](https://a
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Yuanhao Cai · Johns Hopkins University <- Tsinghua · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2cd481bcb426a3b9, topic:transformer