This repository provides the code for the following papers.

Long verison:

Deep Unrolling for Computational Spectral Imaging, IEEE TPAMI (under review).

Short verison:

DNU: Deep Non-local Unrolling for Computational Spectral Imaging, CVPR 2020.

Firstly, use Anaconda to create a virtual Python 3.9 environment with necessary dependencies from the pytorch_environment.yaml file in the code.

conda env create -f ./pytorch_environment.yaml

Then, activate the created environment and continue to train or test.

To train the DNU model for hyperspectral imaging, the datasets should be downloaded to your computer in advance. (e.g., CAVE, KAIST, ICVL, and Harvard.)

In synthetic simulations, for ICVL dataset, you can randomly select 100 spectral images for training and 50 spectral images for testing. For Harvard dataset, you should remove 6 deteriorated spectral images with large-area saturated pixels, and randomly select 35 spectral images for training and 9 spectral images for testing. The training and test images in the ICVL dataset and Harvard dataset are 48 * 48 inclined image blocks.

In semi-physical simulations, the CAVE dataset is used for training and the KAIST dataset is used for test. Then, you should modify the original CAVE and KAIST datasets by spectral interpolation, which have 28 spectral bands ranging from 450nm to 650nm. The patch size for training is 48 * 48, and the patch size for test is 256 * 256.

The Cu_48.mat and mask.mat in ./mask/ are used for synthetic simulations and semi-physical simulations respectively.

Finally, edit the DATASET_PATH and MASK_PATH in train.py to indicate the name and path to your dataset and mask. Here is an example:

DATASET_PATH = "/PATH/DATSET", 
MASK_PATH = "/PATH/MASK", 
}

And there should be two directories in your dataset path: [train, test] to indicate which part should be used for training and testing.

After the dataset is prepared, configure the dataset_name, mask_name and dataset_loader_func_name in train.py and test.py .

The dataset_loader_func_name can be any function provided in datasets.py or any function you implement using Pytorch Dataset. (You should also put your customized dataset loader function in datasets.py and set the dataset_loader_func_name to your customized function name. So that the model can automatically import and use it.)

Current train.py and test_ICVL.py have already provided an example configuration for training using the ICVL dataset.

After the configuration, the training can be started with the following commands:

python train.py

When the training starts, the trainer will save checkpoints into ./ckpt/

The checkpoint of a DNU model trained on ICVL is provided in ./ckpts_ICVL/. You can directly apply ep_160.pth to subsequent test.

After training, reconstruction image can be generated using the following commands:

# For Harvard dataset, using the test set
python test_Harvard.py 

# For ICVL dataset, using the test set
python test_ICVL.py 

# For KAIST dataset, using the test set
python test_KAIST.py 

Then you can obtain test metrics and visualized sRGB images by matlab codes in ./visual.

If our code is useful in your reseach work, please consider citing our paper.

@inproceedings{wang2020dnu,
  title={DNU: deep non-local unrolling for computational spectral imaging},
  author={Wang, Lizhi and Sun, Chen and Zhang, Maoqing and Fu, Ying and Huang, Hua},
  booktitle={CVPR},
  pages={1661--1671},
  year={2020}
}