Imaging through Atmospheric Turbulence

Overview

Imaging through atmospheric turbulence is a fundamental problem for long-range imaging systems. Purdue i2Lab has specialities in several aspects of the subject:

Wave optics theory
Turbulence simulators
Forward imaging models that can be used for deep neural networks
Image reconstruction algorithms
Object recognition

CVPR 2021 Invited Talk (PDF slides)

Turbulence Simulator v1: Multi-aperture simulator

Publication:

Nicholas Chimitt and Stanley H. Chan, ‘‘Simulating Anisoplanatic Turbulence by Sampling Correlated Zernike Coefficients’’, Optical Engineering, 59(8), 083101, July 2020. (A shorter version is presented in IEEE ICCP 2020.)

Code:

MATLAB download: (URL)
Python download: (URL)
Licence: Copyright is granted for educational and research purposes. Please contact Prof Chan for licensing.

Project Page: project_turbulence_TurbSim_v1.html

Turbulence Simulator v2: Phase-to-space transform

Publication:

Zhiyuan Mao, Nicholas Chimitt and Stanley H. Chan, ‘‘Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform’’, accepted to IEEE International Conference on Computer Vision, 2021.

Code:

Python download: (URL)
Licence: Copyright is granted for educational and research purposes. Please contact Prof Chan for licensing.

Project Page: project_turbulence_TurbSim_v2.html

Turbulence Reconstruction

Publication:

Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan, ‘‘Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence’’, IEEE Transactions on Computational Imaging, vol. 6, pp. 1415-1428, Oct. 2020.

Code:

MATLAB download: (URL)
Licence: Copyright is granted for educational and research purposes. Please contact Prof Chan for licensing.

Project Page: project_turbulence_Recon_v1.html