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:
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Project page
https://engineering.purdue.edu/ChanGroup/project_turbulence.html
-
Simulator:
LICENSE
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.