Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence

This repository contains the code for the following paper:

Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan, ‘‘Image reconstruction of static and dynamic scenes through anisoplanatic turbulence’’, IEEE Trans. Computational Imaging, vol. 6, pp. 1415-1428, Oct. 2020.

How to use:

• Check the demo.m file
• We've prepared a few sets of parameters for low, medium and high turbulence level (corresponding to D/r0 = 1.5, 3, and 4.5)

Packages included

• Algorithm for image reconstruction through atmospheric turbulence, developed by Purdue i2Lab
• Plug and Play ADMM, developed by Purdue i2Lab
• Optical flow, developed by Ce Liu (Microsoft Research New England) https://people.csail.mit.edu/celiu/OpticalFlow/

The data used in the paper can be downloaded here:

https://drive.google.com/file/d/1VsQyrPexjAXegAXAx7A5CmKB0F4hw_O7/view?usp=sharing

If you find our work helpful in your research, please consider cite our paper

@ARTICLE{mao_tci,
  author={Zhiyuan Mao and Nicholas Chimitt and Stanley H. Chan},
  journal={IEEE Transactions on Computational Imaging}, 
  title={Image Reconstruction of Static and Dynamic Scenes Through Anisoplanatic Turbulence}, 
  year={2020},
  volume={6},
  pages={1415-1428},
  doi={10.1109/TCI.2020.3029401}
  }

Please also check out our other work on Atmospheric Turbulence:

• Project page

  https://engineering.purdue.edu/ChanGroup/project_turbulence.html

• Simulator:

  https://www.mathworks.com/matlabcentral/fileexchange/78742-atmospheric-turbulence-simulator-for-image-reconstruction

LICENSE

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.