Awesome

A Toolbox for Spectral Compressive Imaging

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)
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (CVPR 2022)
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction (ECCV 2022)
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging (NeurIPS 2022)
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging (CVPR 2022)

Awards

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.

News

2024.04.09 : We release the results of the three traditional model-based methods, i.e., TwIST, GAP-TV, and DeSCI for your convenience to conduct research. Feel free to use them. 😄
2024.03.21 : Our methods Retinexformer and MST++ (NTIRE 2022 Spectral Reconstruction Challenge Winner) ranked top-2 in the NTIRE 2024 Challenge on Low Light Enhancement. Code, pre-trained models, training logs, and enhancement results will be released in the repo of Retinexformer. Stay tuned! 🚀
2024.02.15 : NTIRE 2024 Challenge on Low Light Enhancement begins. Welcome to use our Retinexformer or MST++ (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 (NeurIPS 2023) are released at this repo. We also develop a toolbox 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 collection. 💫
2023.09.21 : Our new work BiSRNet is accepted by NeurIPS 23. Code will be released at this repo and BiSCI
2023.02.26 : We release the RGB images of five real scenes and ten simulation scenes. Please feel free to check and use them. 🌟
2022.11.02 : We have provided more visual results of state-of-the-art methods and the function to evaluate the parameters and computational complexity of models. Please feel free to check and use them. :high_brightness:
2022.10.23 : Code, models, and reconstructed HSI results of DAUHST have been released. 🔥
2022.09.15 : Our DAUHST has been accepted by NeurIPS 2022, code and models are coming soon. :rocket:
2022.07.20 : Code, models, and reconstructed HSI results of CST have been released. 🔥
2022.07.04 : Our paper CST has been accepted by ECCV 2022, code and models are coming soon. :rocket:
2022.06.14 : Code and models of MST and MST++ have been released. This repo supports 12 learning-based methods to serve as toolbox for Spectral Compressive Imaging. The model zoo will be enlarged. 🔥
2022.05.20 : Our work DAUHST is on arxiv. :dizzy:
2022.04.02 : Further work MST++ has won the NTIRE 2022 Spectral Reconstruction Challenge. :trophy:
2022.03.09 : Our work CST is on arxiv. :dizzy:
2022.03.02 : Our paper MST has been accepted by CVPR 2022, code and models are coming soon. :rocket:

Scene 2	Scene 3	Scene 4	Scene 7
<img src="./figure/frame2channel12.gif" height=170 width=170>	<img src="./figure/frame3channel21.gif" width=170 height=170>	<img src="./figure/frame4channel28.gif" width=170 height=170>	<img src="./figure/frame7channel4.gif" width=170 height=170>

1. Comparison with State-of-the-art Methods

12 learning-based algorithms and 3 model-based methods are supported.

<details open> <summary><b>Supported algorithms:</b></summary> </details>

We are going to enlarge our model zoo in the future.

MST vs. SOTA	CST vs. MST
<img src="./figure/compare_fig.png" height=320>	<img src="./figure/cst_mst.png" height=320>
MST++ vs. SOTA	DAUHST vs. SOTA
<img src="./figure/mst_pp.png" height=320>	<img src="./figure/dauhst.png" height=320>

BiSRNet vs. SOTA BNNs
<img src="./figure/bisrnet.png" width=812>

Quantitative Comparison on Simulation Dataset

Method	Params (M)	FLOPS (G)	PSNR	SSIM	Model Zoo	Simulation Result	Real Result
TwIST	-	-	23.12	0.669	-	Google Drive / Baidu Disk	Google Drive / Baidu Disk
GAP-TV	-	-	24.36	0.669	-	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DeSCI	-	-	25.27	0.721	-	Google Drive / Baidu Disk	Google Drive / Baidu Disk
λ-Net	62.64	117.98	28.53	0.841	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
TSA-Net	44.25	110.06	31.46	0.894	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DGSMP	3.76	646.65	32.63	0.917	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
GAP-Net	4.27	78.58	33.26	0.917	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
ADMM-Net	4.27	78.58	33.58	0.918	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
BIRNAT	4.40	2122.66	37.58	0.960	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
HDNet	2.37	154.76	34.97	0.943	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
MST-S	0.93	12.96	34.26	0.935	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
MST-M	1.50	18.07	34.94	0.943	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
MST-L	2.03	28.15	35.18	0.948	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
MST++	1.33	19.42	35.99	0.951	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
CST-S	1.20	11.67	34.71	0.940	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
CST-M	1.36	16.91	35.31	0.947	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
CST-L	3.00	27.81	35.85	0.954	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
CST-L-Plus	3.00	40.10	36.12	0.957	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAUHST-2stg	1.40	18.44	36.34	0.952	Google Drive / Baidu Disk	Google Drive /Baidu Disk	Google Drive / Baidu Disk
DAUHST-3stg	2.08	27.17	37.21	0.959	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAUHST-5stg	3.44	44.61	37.75	0.962	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAUHST-9stg	6.15	79.50	38.36	0.967	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk
BiSRNet	0.036	1.18	29.76	0.837	Google Drive / Baidu Disk	Google Drive / Baidu Disk	Google Drive / Baidu Disk

The performance are reported on 10 scenes of the KAIST dataset. The test size of FLOPS is 256 x 256.

We also provide the RGB images of five real scenes and ten simulation scenes for your convenience to draw a figure.

Note: access code for Baidu Disk is mst1

2. Create Environment:

Python 3 (Recommend to use Anaconda)
NVIDIA GPU + CUDA
Python packages:

  pip install -r requirements.txt

3. Prepare Dataset:

Download cave_1024_28 (Baidu Disk, code: fo0q | One Drive), CAVE_512_28 (Baidu Disk, code: ixoe | One Drive), KAIST_CVPR2021 (Baidu Disk, code: 5mmn | One Drive), TSA_simu_data (Baidu Disk, code: efu8 | One Drive), TSA_real_data (Baidu Disk, code: eaqe | One Drive), and then put them into the corresponding folders of datasets/ and recollect them as the following form:

|--MST
    |--real
    	|-- test_code
    	|-- train_code
    |--simulation
    	|-- test_code
    	|-- train_code
    |--visualization
    |--datasets
        |--cave_1024_28
            |--scene1.mat
            |--scene2.mat
            ：  
            |--scene205.mat
        |--CAVE_512_28
            |--scene1.mat
            |--scene2.mat
            ：  
            |--scene30.mat
        |--KAIST_CVPR2021  
            |--1.mat
            |--2.mat
            ： 
            |--30.mat
        |--TSA_simu_data  
            |--mask.mat   
            |--Truth
                |--scene01.mat
                |--scene02.mat
                ： 
                |--scene10.mat
        |--TSA_real_data  
            |--mask.mat   
            |--Measurements
                |--scene1.mat
                |--scene2.mat
                ： 
                |--scene5.mat

Following TSA-Net and DGSMP, we use the CAVE dataset (cave_1024_28) as the simulation training set. Both the CAVE (CAVE_512_28) and KAIST (KAIST_CVPR2021) datasets are used as the real training set.

4. Simulation Experiement:

4.1　Training

cd MST/simulation/train_code/

# MST_S
python train.py --template mst_s --outf ./exp/mst_s/ --method mst_s 

# MST_M
python train.py --template mst_m --outf ./exp/mst_m/ --method mst_m  

# MST_L
python train.py --template mst_l --outf ./exp/mst_l/ --method mst_l 

# CST_S
python train.py --template cst_s --outf ./exp/cst_s/ --method cst_s 

# CST_M
python train.py --template cst_m --outf ./exp/cst_m/ --method cst_m  

# CST_L
python train.py --template cst_l --outf ./exp/cst_l/ --method cst_l

# CST_L_Plus
python train.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus

# GAP-Net
python train.py --template gap_net --outf ./exp/gap_net/ --method gap_net 

# ADMM-Net
python train.py --template admm_net --outf ./exp/admm_net/ --method admm_net 

# TSA-Net
python train.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net 

# HDNet
python train.py --template hdnet --outf ./exp/hdnet/ --method hdnet 

# DGSMP
python train.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp 

# BIRNAT
python train.py --template birnat --outf ./exp/birnat/ --method birnat 

# MST_Plus_Plus
python train.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus 

# λ-Net
python train.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net

# DAUHST-2stg
python train.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg

# DAUHST-3stg
python train.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg

# DAUHST-5stg
python train.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg

# DAUHST-9stg
python train.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg

# BiSRNet
python train.py --template bisrnet --outf ./exp/bisrnet/ --method bisrnet

The training log, trained model, and reconstrcuted HSI will be available in MST/simulation/train_code/exp/

4.2　Testing

Download the pretrained model zoo from (Google Drive / Baidu Disk, code: mst1) and place them to MST/simulation/test_code/model_zoo/

Run the following command to test the model on the simulation dataset.

cd MST/simulation/test_code/

# MST_S
python test.py --template mst_s --outf ./exp/mst_s/ --method mst_s --pretrained_model_path ./model_zoo/mst/mst_s.pth

# MST_M
python test.py --template mst_m --outf ./exp/mst_m/ --method mst_m --pretrained_model_path ./model_zoo/mst/mst_m.pth

# MST_L
python test.py --template mst_l --outf ./exp/mst_l/ --method mst_l --pretrained_model_path ./model_zoo/mst/mst_l.pth

# CST_S
python test.py --template cst_s --outf ./exp/cst_s/ --method cst_s --pretrained_model_path ./model_zoo/cst/cst_s.pth

# CST_M
python test.py --template cst_m --outf ./exp/cst_m/ --method cst_m --pretrained_model_path ./model_zoo/cst/cst_m.pth

# CST_L
python test.py --template cst_l --outf ./exp/cst_l/ --method cst_l --pretrained_model_path ./model_zoo/cst/cst_l.pth

# CST_L_Plus
python test.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus --pretrained_model_path ./model_zoo/cst/cst_l_plus.pth

# GAP_Net
python test.py --template gap_net --outf ./exp/gap_net/ --method gap_net --pretrained_model_path ./model_zoo/gap_net/gap_net.pth

# ADMM_Net
python test.py --template admm_net --outf ./exp/admm_net/ --method admm_net --pretrained_model_path ./model_zoo/admm_net/admm_net.pth

# TSA_Net
python test.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net --pretrained_model_path ./model_zoo/tsa_net/tsa_net.pth

# HDNet
python test.py --template hdnet --outf ./exp/hdnet/ --method hdnet --pretrained_model_path ./model_zoo/hdnet/hdnet.pth

# DGSMP
python test.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp --pretrained_model_path ./model_zoo/dgsmp/dgsmp.pth

# BIRNAT
python test.py --template birnat --outf ./exp/birnat/ --method birnat --pretrained_model_path ./model_zoo/birnat/birnat.pth

# MST_Plus_Plus
python test.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus --pretrained_model_path ./model_zoo/mst_plus_plus/mst_plus_plus.pth

# λ-Net
python test.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net --pretrained_model_path ./model_zoo/lambda_net/lambda_net.pth

# DAUHST-2stg
python test.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg --pretrained_model_path ./model_zoo/dauhst_2stg/dauhst_2stg.pth

# DAUHST-3stg
python test.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg --pretrained_model_path ./model_zoo/dauhst_3stg/dauhst_3stg.pth

# DAUHST-5stg
python test.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg --pretrained_model_path ./model_zoo/dauhst_5stg/dauhst_5stg.pth

# DAUHST-9stg
python test.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg --pretrained_model_path ./model_zoo/dauhst_9stg/dauhst_9stg.pth

# BiSRNet
python test.py --template bisrnet --outf ./exp/bisrnet/ --method bisrnet --pretrained_model_path ./model_zoo/bisrnet/bisrnet.pth

The reconstrcuted HSIs will be output into MST/simulation/test_code/exp/. Then place the reconstructed results into MST/simulation/test_code/Quality_Metrics/results and run the following MATLAB command to calculate the PSNR and SSIM of the reconstructed HSIs.

Run cal_quality_assessment.m

Evaluating the Params and FLOPS of models

We provide two functions my_summary() and my_summary_bnn() in simulation/test_code/utils.py. Use them to evaluate the parameters and FLOPS of full-precision and binarized models

from utils import my_summary, my_summary_bnn
my_summary(MST(), 256, 256, 28, 1)
my_summary_bnn(BiSRNet(), 256, 256, 28, 1)

4.3　Visualization

Put the reconstruted HSI in MST/visualization/simulation_results/results and rename it as method.mat, e.g., mst_s.mat.
Generate the RGB images of the reconstructed HSIs

 cd MST/visualization/
 Run show_simulation.m

Draw the spetral density lines

cd MST/visualization/
Run show_line.m

5. Real Experiement:

5.1　Training

cd MST/real/train_code/

# MST_S
python train.py --template mst_s --outf ./exp/mst_s/ --method mst_s 

# MST_M
python train.py --template mst_m --outf ./exp/mst_m/ --method mst_m  

# MST_L
python train.py --template mst_l --outf ./exp/mst_l/ --method mst_l 

# CST_S
python train.py --template cst_s --outf ./exp/cst_s/ --method cst_s 

# CST_M
python train.py --template cst_m --outf ./exp/cst_m/ --method cst_m  

# CST_L
python train.py --template cst_l --outf ./exp/cst_l/ --method cst_l

# CST_L_Plus
python train.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus

# GAP-Net
python train.py --template gap_net --outf ./exp/gap_net/ --method gap_net 

# ADMM-Net
python train.py --template admm_net --outf ./exp/admm_net/ --method admm_net 

# TSA-Net
python train.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net 

# HDNet
python train.py --template hdnet --outf ./exp/hdnet/ --method hdnet 

# DGSMP
python train.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp 

# BIRNAT
python train.py --template birnat --outf ./exp/birnat/ --method birnat 

# MST_Plus_Plus
python train.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus 

# λ-Net
python train.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net

# DAUHST-2stg
python train.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg

# DAUHST-3stg
python train.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg

# DAUHST-5stg
python train.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg

# DAUHST-9stg
python train.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg

# BiSRNet
python train_s.py --outf ./exp/bisrnet/ --method bisrnet

If you do not have a large memory GPU, add --size 128 to use a small patch size.
The training log, trained model, and reconstrcuted HSI will be available in MST/real/train_code/exp/
Note: you can use train_s.py for other methods except BiSRNet if you cannot access the mask data or you have limited GPU resources. In this case, you need to replace the --method paramter in the above commands and make some modifications.

5.2　Testing

The pretrained model of BiSRNet can be download from (Google Drive / Baidu Disk, code: mst1) and place them to MST/real/test_code/model_zoo/

cd MST/real/test_code/

# MST_S
python test.py --outf ./exp/mst_s/ --pretrained_model_path ./model_zoo/mst/mst_s.pth

# MST_M
python test.py --outf ./exp/mst_m/ --pretrained_model_path ./model_zoo/mst/mst_m.pth

# MST_L
python test.py  --outf ./exp/mst_l/ --pretrained_model_path ./model_zoo/mst/mst_l.pth

# CST_S
python test.py --outf ./exp/cst_s/ --pretrained_model_path ./model_zoo/cst/cst_s.pth

# CST_M
python test.py --outf ./exp/cst_m/ --pretrained_model_path ./model_zoo/cst/cst_m.pth

# CST_L
python test.py --outf ./exp/cst_l/ --pretrained_model_path ./model_zoo/cst/cst_l.pth

# CST_L_Plus
python test.py --outf ./exp/cst_l_plus/ --pretrained_model_path ./model_zoo/cst/cst_l_plus.pth

# GAP_Net
python test.py --outf ./exp/gap_net/ --pretrained_model_path ./model_zoo/gap_net/gap_net.pth

# ADMM_Net
python test.py --outf ./exp/admm_net/ --pretrained_model_path ./model_zoo/admm_net/admm_net.pth

# TSA_Net
python test.py --outf ./exp/tsa_net/ --pretrained_model_path ./model_zoo/tsa_net/tsa_net.pth

# HDNet
python test.py --template hdnet --outf ./exp/hdnet/ --method hdnet --pretrained_model_path ./model_zoo/hdnet/hdnet.pth

# DGSMP
python test.py --outf ./exp/dgsmp/ --pretrained_model_path ./model_zoo/dgsmp/dgsmp.pth

# BIRNAT
python test.py --outf ./exp/birnat/ --pretrained_model_path ./model_zoo/birnat/birnat.pth

# MST_Plus_Plus
python test.py --outf ./exp/mst_plus_plus/ --pretrained_model_path ./model_zoo/mst_plus_plus/mst_plus_plus.pth

# λ-Net
python test.py --outf ./exp/lambda_net/ --pretrained_model_path ./model_zoo/lambda_net/lambda_net.pth

# DAUHST_2stg
python test.py --outf ./exp/dauhst_2stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_2stg.pth

# DAUHST_3stg
python test.py --outf ./exp/dauhst_3stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_3stg.pth

# DAUHST_5stg
python test.py --outf ./exp/dauhst_5stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_5stg.pth

# DAUHST_9stg
python test.py --outf ./exp/dauhst_9stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_9stg.pth

# BiSRNet
python test.py --outf ./exp/bisrnet  --pretrained_model_path ./model_zoo/bisrnet/bisrnet.pth --method bisrnet

The reconstrcuted HSI will be output into MST/real/test_code/exp/

5.3　Visualization

Put the reconstruted HSI in MST/visualization/real_results/results and rename it as method.mat, e.g., mst_plus_plus.mat.
Generate the RGB images of the reconstructed HSI

cd MST/visualization/
Run show_real.m

6. Citation

If this repo helps you, please consider citing our works:



# MST
@inproceedings{mst,
  title={Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction},
  author={Yuanhao Cai and Jing Lin and Xiaowan Hu and Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool},
  booktitle={CVPR},
  year={2022}
}


# CST
@inproceedings{cst,
  title={Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction},
  author={Yuanhao Cai and Jing Lin and Xiaowan Hu and Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool},
  booktitle={ECCV},
  year={2022}
}


# DAUHST
@inproceedings{dauhst,
  title={Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging},
  author={Yuanhao Cai and Jing Lin and Haoqian Wang and Xin Yuan and Henghui Ding and Yulun Zhang and Radu Timofte and Luc Van Gool},
  booktitle={NeurIPS}, 
  year={2022}
}


# BiSCI
@inproceedings{bisci,
  title={Binarized Spectral Compressive Imaging},
  author={Yuanhao Cai and Yuxin Zheng and Jing Lin and Xin Yuan and Yulun Zhang and Haoqian Wang},
  booktitle={NeurIPS},
  year={2023}
}


# MST++
@inproceedings{mst_pp,
  title={MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction},
  author={Yuanhao Cai and Jing Lin and Zudi Lin and Haoqian Wang and Yulun Zhang and Hanspeter Pfister and Radu Timofte and Luc Van Gool},
  booktitle={CVPRW},
  year={2022}
}


# HDNet
@inproceedings{hdnet,
  title={HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging},
  author={Xiaowan Hu and Yuanhao Cai and Jing Lin and  Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool},
  booktitle={CVPR},
  year={2022}
}