DDS

Hyungjin Chung, Suhyeon Lee, Jong Chul Ye

Official PyTorch implementation for Decomposed Diffusion Sampling (DDS), presented in the paper Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems.

Getting Started

Download pre-trained modei weights

# Download pre-trained model weights
mkdir -p './exp/vp'
wget -O './exp/vp/AAPM256_1M.pth' 'https://www.dropbox.com/scl/fi/hs75uqc9zgwi8en71g488/AAPM256_1M.pt?rlkey=s1otrarzz1tbr7j6nwc5rh1r6&dl=1'
wget -O './exp/vp/fastmri_brain_320_complex_1m.pth' 'https://www.dropbox.com/scl/fi/1884de5g904fch2bah4bl/fastmri_knee_320_complex_1m.pt?rlkey=qukkxajxp6broc1fn613cdvp2&dl=1'

Download sample test data

# Download sample test data
mkdir -p './indist_samples'
wget -O './indist_samples/data.zip' 'https://www.dropbox.com/scl/fo/rlxuhs5maial9cnwc533d/h?rlkey=9zovjgghncnt8ej4acdsb1a5d&dl=1'
# Extract zip file
unzip -q ./indist_samples/data.zip -d ./indist_samples

By default, the above scripts places the pre-trained model checkpoints under exp/vp, and the sample data under indist_samples. When saving to different directories, you can reflect this by modifying the --dataset_path flag for the main.py file.

DDS reconstruction

We provide demo scripts for the following inverse problems:

• Multi-coil MRI reconstruction (2d)
• Sparse-view CT reconstruction (2d, 3d)
• Limited-angle CT reconstruction (3d)

Each experiments can be run by simply running

bash eval_MRI_multi.sh
bash eval_SVCT_2d.sh
bash eval_SVCT_3d.sh
bash eval_LACT_3d.sh

Citation

If you find our work interesting, please consider citing

@article{chung2023decomposed,
  title={Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems},
  author={Chung, Hyungjin and Lee, Suhyeon and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2303.05754},
  year={2023}
}