abao1999 / DynamicDPI

Reconstructing video of quickly evolving sources, using Deep Probabilistic Imaging (DPI) in message passing

DynamicDPI

Reconstructing video of quickly evolving sources, with uncertainty quantification, using Deep Probabilistic Imaging (DPI) in a message passing protocol on a probabilstic graphical model of the hidden images. Case studies include VLBI and Dynamic MRI.

Run Examples

TODO

Requirements

General requirements for PyTorch release:

• pytorch
• torchkbnufft

For radio interferometric imaging:

• eht-imaging
• astropy
• pyNFFT

Please check DPI.yml for the detailed Anaconda environment information. TensorFlow release is coming soon!

Citations

DPI

@inproceedings{sun2021deep,
    author = {He Sun and Katherine L. Bouman},
    title = {Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging},
    booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
    year = {2021},
}

StarWarps

K. L. Bouman et al., "Reconstructing Video of Time-Varying Sources From Radio Interferometric Measurements," in IEEE Transactions on Computational Imaging, vol. 4, no. 4, pp. 512-527, Dec. 2018, doi: 10.1109/TCI.2018.2838452.