MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)

Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc Van Gool

The first two authors contribute equally to this work

News

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! 🏆
2023.11.02 : Our MST++ is added to the Awesome-Transformer-Attention collection. 💫
2022.10.24 : We have provided Params and FLOPS evaluating function. Feel free to check and use them.
2022.10.23 : We have provided some visualization tool functions. Please feel free to check and use them.
2022.04.17 : Our paper has been accepted by CVPRW 2022, code and models have been released. 🚀
2022.04.02 : We win the First place of NTIRE 2022 Challenge on Spectral Reconstruction from RGB. 🏆

480 nm	520 nm	580 nm	660 nm

Abstract: Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is based on the HSI spatially sparse while spectrally self-similar nature to compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure to extract multi-resolution contextual information. Finally, our MST++, cascaded by several SSTs, progressively improves the reconstruction quality from coarse to fine. Comprehensive experiments show that our MST++ significantly outperforms other state-of-the-art methods. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place.

Network Architecture

Our MST++ is mainly based on our work MST, which is accepted by CVPR 2022.

Comparison with State-of-the-art Methods

This repo is a baseline and toolbox containing 11 image restoration algorithms for Spectral Reconstruction.

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

Supported algorithms:

Results on NTIRE 2022 HSI Dataset - Validation

Method	Params (M)	FLOPS (G)	MRAE	RMSE	PSNR	Model Zoo
HSCNN+	4.65	304.45	0.3814	0.0588	26.36	Google Drive / Baidu Disk
HRNet	31.70	163.81	0.3476	0.0550	26.89	Google Drive / Baidu Disk
EDSR	2.42	158.32	0.3277	0.0437	28.29	Google Drive / Baidu Disk
AWAN	4.04	270.61	0.2500	0.0367	31.22	Google Drive / Baidu Disk
HDNet	2.66	173.81	0.2048	0.0317	32.13	Google Drive / Baidu Disk
HINet	5.21	31.04	0.2032	0.0303	32.51	Google Drive / Baidu Disk
MIRNet	3.75	42.95	0.1890	0.0274	33.29	Google Drive / Baidu Disk
Restormer	15.11	93.77	0.1833	0.0274	33.40	Google Drive / Baidu Disk
MPRNet	3.62	101.59	0.1817	0.0270	33.50	Google Drive / Baidu Disk
MST-L	2.45	32.07	0.1772	0.0256	33.90	Google Drive / Baidu Disk
MST++	1.62	23.05	0.1645	0.0248	34.32	Google Drive / Baidu Disk

Our MST++ siginificantly outperforms other methods while requiring cheaper Params and FLOPS.

Note: access code for Baidu Disk is mst1.

1. Create Envirement:

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

Python packages:

cd MST-plus-plus
pip install -r requirements.txt

2. Data Preparation:

Download training spectral images (Google Drive / Baidu Disk, code: mst1), training RGB images (Google Drive / Baidu Disk), validation spectral images (Google Drive / Baidu Disk), validation RGB images (Google Drive / Baidu Disk), and testing RGB images (Google Drive / Baidu Disk) from the competition website of NTIRE 2022 Spectral Reconstruction Challenge.
Place the training spectral images and validation spectral images to /MST-plus-plus/dataset/Train_Spec/.
Place the training RGB images and validation RGB images to /MST-plus-plus/dataset/Train_RGB/.
Place the testing RGB images to /MST-plus-plus/dataset/Test_RGB/.

Then this repo is collected as the following form:

|--MST-plus-plus
    |--test_challenge_code
    |--test_develop_code
    |--train_code  
    |--dataset 
        |--Train_Spec
            |--ARAD_1K_0001.mat
            |--ARAD_1K_0002.mat
            ： 
            |--ARAD_1K_0950.mat
  	|--Train_RGB
            |--ARAD_1K_0001.jpg
            |--ARAD_1K_0002.jpg
            ： 
            |--ARAD_1K_0950.jpg
        |--Test_RGB
            |--ARAD_1K_0951.jpg
            |--ARAD_1K_0952.jpg
            ： 
            |--ARAD_1K_1000.jpg
        |--split_txt
            |--train_list.txt
            |--valid_list.txt

note: If you train on your custom dataset, please use the Loss_MRAE_custom function in train_code/utils.py to avoid Nan problem

3. Evaluation on the Validation Set:

(1) Download the pretrained model zoo from (Google Drive / Baidu Disk, code: mst1) and place them to /MST-plus-plus/test_develop_code/model_zoo/.

(2) Run the following command to test the model on the validation RGB images.

cd /MST-plus-plus/test_develop_code/

# test MST++
python test.py --data_root ../dataset/  --method mst_plus_plus --pretrained_model_path ./model_zoo/mst_plus_plus.pth --outf ./exp/mst_plus_plus/  --gpu_id 0

# test MST-L
python test.py --data_root ../dataset/  --method mst --pretrained_model_path ./model_zoo/mst.pth --outf ./exp/mst/  --gpu_id 0

# test MIRNet
python test.py --data_root ../dataset/  --method mirnet --pretrained_model_path ./model_zoo/mirnet.pth --outf ./exp/mirnet/  --gpu_id 0

# test HINet
python test.py --data_root ../dataset/  --method hinet --pretrained_model_path ./model_zoo/hinet.pth --outf ./exp/hinet/  --gpu_id 0

# test MPRNet
python test.py --data_root ../dataset/  --method mprnet --pretrained_model_path ./model_zoo/mprnet.pth --outf ./exp/mprnet/  --gpu_id 0

# test Restormer
python test.py --data_root ../dataset/  --method restormer --pretrained_model_path ./model_zoo/restormer.pth --outf ./exp/restormer/  --gpu_id 0

# test EDSR
python test.py --data_root ../dataset/  --method edsr --pretrained_model_path ./model_zoo/edsr.pth --outf ./exp/edsr/  --gpu_id 0

# test HDNet
python test.py --data_root ../dataset/  --method hdnet --pretrained_model_path ./model_zoo/hdnet.pth --outf ./exp/hdnet/  --gpu_id 0

# test HRNet
python test.py --data_root ../dataset/  --method hrnet --pretrained_model_path ./model_zoo/hrnet.pth --outf ./exp/hrnet/  --gpu_id 0

# test HSCNN+
python test.py --data_root ../dataset/  --method hscnn_plus --pretrained_model_path ./model_zoo/hscnn_plus.pth --outf ./exp/hscnn_plus/  --gpu_id 0

# test AWAN
python test.py --data_root ../dataset/  --method awan --pretrained_model_path ./model_zoo/awan.pth --outf ./exp/awan/  --gpu_id 0

The results will be saved in /MST-plus-plus/test_develop_code/exp/ in the mat format and the evaluation metric (including MRAE,RMSE,PSNR) will be printed.

Evaluating the Params and FLOPS of models

We have provided a function my_summary() in test_develop_code/utils.py, please use this function to evaluate the parameters and computational complexity of the models, especially the Transformers as

from utils import my_summary
my_summary(MST_Plus_Plus(), 256, 256, 3, 1)

4. Evaluation on the Test Set:

(1) Download the pretrained model zoo from (Google Drive / Baidu Disk, code: mst1) and place them to /MST-plus-plus/test_challenge_code/model_zoo/.

(2) Run the following command to test the model on the testing RGB images.

cd /MST-plus-plus/test_challenge_code/

# test MST++
python test.py --data_root ../dataset/  --method mst_plus_plus --pretrained_model_path ./model_zoo/mst_plus_plus.pth --outf ./exp/mst_plus_plus/  --gpu_id 0

# test MST-L
python test.py --data_root ../dataset/  --method mst --pretrained_model_path ./model_zoo/mst.pth --outf ./exp/mst/  --gpu_id 0

# test MIRNet
python test.py --data_root ../dataset/  --method mirnet --pretrained_model_path ./model_zoo/mirnet.pth --outf ./exp/mirnet/  --gpu_id 0

# test HINet
python test.py --data_root ../dataset/  --method hinet --pretrained_model_path ./model_zoo/hinet.pth --outf ./exp/hinet/  --gpu_id 0

# test MPRNet
python test.py --data_root ../dataset/  --method mprnet --pretrained_model_path ./model_zoo/mprnet.pth --outf ./exp/mprnet/  --gpu_id 0

# test Restormer
python test.py --data_root ../dataset/  --method restormer --pretrained_model_path ./model_zoo/restormer.pth --outf ./exp/restormer/  --gpu_id 0

# test EDSR
python test.py --data_root ../dataset/  --method edsr --pretrained_model_path ./model_zoo/edsr.pth --outf ./exp/edsr/  --gpu_id 0

# test HDNet
python test.py --data_root ../dataset/  --method hdnet --pretrained_model_path ./model_zoo/hdnet.pth --outf ./exp/hdnet/  --gpu_id 0

# test HRNet
python test.py --data_root ../dataset/  --method hrnet --pretrained_model_path ./model_zoo/hrnet.pth --outf ./exp/hrnet/  --gpu_id 0

# test HSCNN+
python test.py --data_root ../dataset/  --method hscnn_plus --pretrained_model_path ./model_zoo/hscnn_plus.pth --outf ./exp/hscnn_plus/  --gpu_id 0

The results and submission.zip will be saved in /MST-plus-plus/test_challenge_code/exp/.

5. Training

To train a model, run

cd /MST-plus-plus/train_code/

# train MST++
python train.py --method mst_plus_plus  --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/mst_plus_plus/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train MST-L
python train.py --method mst  --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/mst/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train MIRNet
python train.py --method mirnet  --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/mirnet/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train HINet
python train.py --method hinet  --batch_size 20 --end_epoch 300 --init_lr 2e-4 --outf ./exp/hinet/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train MPRNet
python train.py --method mprnet  --batch_size 20 --end_epoch 300 --init_lr 2e-4 --outf ./exp/mprnet/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train Restormer
python train.py --method restormer  --batch_size 20 --end_epoch 300 --init_lr 2e-4 --outf ./exp/restormer/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train EDSR
python train.py --method edsr  --batch_size 20 --end_epoch 300 --init_lr 1e-4 --outf ./exp/edsr/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train HDNet
python train.py --method hdnet  --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/hdnet/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train HRNet
python train.py --method hrnet  --batch_size 20 --end_epoch 300 --init_lr 1e-4 --outf ./exp/hrnet/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train HSCNN+
python train.py --method hscnn_plus  --batch_size 20 --end_epoch 300 --init_lr 2e-4 --outf ./exp/hscnn_plus/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

# train AWAN
python train.py --method awan  --batch_size 20 --end_epoch 300 --init_lr 1e-4 --outf ./exp/awan/ --data_root ../dataset/  --patch_size 128 --stride 8  --gpu_id 0

The training log and models will be saved in /MST-plus-plus/train_code/exp/.

6. Prediction

(1) Download the pretrained model zoo from (Google Drive / Baidu Disk, code: mst1) and place them to /MST-plus-plus/predict_code/model_zoo/.

(2) Run the following command to reconstruct your own RGB image.

cd /MST-plus-plus/predict_code/

# reconstruct by MST++
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method mst_plus_plus --pretrained_model_path ./model_zoo/mst_plus_plus.pth --outf ./exp/mst_plus_plus/  --gpu_id 0

# reconstruct by MST-L
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method mst --pretrained_model_path ./model_zoo/mst.pth --outf ./exp/mst/  --gpu_id 0

# reconstruct by MIRNet
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method mirnet --pretrained_model_path ./model_zoo/mirnet.pth --outf ./exp/mirnet/  --gpu_id 0

# reconstruct by HINet
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method hinet --pretrained_model_path ./model_zoo/hinet.pth --outf ./exp/hinet/  --gpu_id 0

# reconstruct by MPRNet
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method mprnet --pretrained_model_path ./model_zoo/mprnet.pth --outf ./exp/mprnet/  --gpu_id 0

# reconstruct by Restormer
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method restormer --pretrained_model_path ./model_zoo/restormer.pth --outf ./exp/restormer/  --gpu_id 0

# reconstruct by EDSR
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg --method edsr --pretrained_model_path ./model_zoo/edsr.pth --outf ./exp/edsr/  --gpu_id 0

# reconstruct by HDNet
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method hdnet --pretrained_model_path ./model_zoo/hdnet.pth --outf ./exp/hdnet/  --gpu_id 0

# reconstruct by HRNet
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method hrnet --pretrained_model_path ./model_zoo/hrnet.pth --outf ./exp/hrnet/  --gpu_id 0

# reconstruct by HSCNN+
python test.py --rgb_path ./demo/ARAD_1K_0912.jpg  --method hscnn_plus --pretrained_model_path ./model_zoo/hscnn_plus.pth --outf ./exp/hscnn_plus/  --gpu_id 0

You can replace './demo/ARAD_1K_0912.jpg' with your RGB image path. The reconstructed results will be saved in /MST-plus-plus/predict_code/exp/.

7. Visualization

Put the reconstruted HSI in visualization/simulation_results/results/.
Generate the RGB images of the reconstructed HSIs

cd visualization/
Run show_simulation.m

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}
}


# 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}
}

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MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)

News

Network Architecture

Comparison with State-of-the-art Methods

Results on NTIRE 2022 HSI Dataset - Validation

1. Create Envirement:

2. Data Preparation:

3. Evaluation on the Validation Set:

Evaluating the Params and FLOPS of models

4. Evaluation on the Test Set:

5. Training

6. Prediction

7. Visualization

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visualization		visualization
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MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)

News

Network Architecture

Comparison with State-of-the-art Methods

Results on NTIRE 2022 HSI Dataset - Validation

1. Create Envirement:

2. Data Preparation:

3. Evaluation on the Validation Set:

Evaluating the Params and FLOPS of models

4. Evaluation on the Test Set:

5. Training

6. Prediction

7. Visualization

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