TY - GEN
T1 - Turbulence-induced 2D correlated image distortion
AU - Schwartzman, Armin
AU - Alterman, Marina
AU - Zamir, Rotem
AU - Schechner, Yoav Y.
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Due to atmospheric turbulence, light randomly refracts in three dimensions (3D), eventually entering a camera at a perturbed angle. Each viewed object point thus has a distorted projection in a two-dimensional (2D) image. Simulating 3D random refraction for all viewed points via complex simulated 3D random turbulence is computationally expensive. We derive an efficient way to render 2D image distortions, consistent with turbulence. Our approach bypasses 3D numerical calculations altogether We directly create 2D random physics-based distortion vector fields, where correlations are derived in closed form from turbulence theory. The correlations are nontrivial: they depend on the perturbation directions relative to the orientation of all object-pairs, simultaneously. Hence, we develop a theory characterizing and rendering such a distortion field. The theory is turned to a few simple 2D operations, which render images based on camera and atmospheric properties.
AB - Due to atmospheric turbulence, light randomly refracts in three dimensions (3D), eventually entering a camera at a perturbed angle. Each viewed object point thus has a distorted projection in a two-dimensional (2D) image. Simulating 3D random refraction for all viewed points via complex simulated 3D random turbulence is computationally expensive. We derive an efficient way to render 2D image distortions, consistent with turbulence. Our approach bypasses 3D numerical calculations altogether We directly create 2D random physics-based distortion vector fields, where correlations are derived in closed form from turbulence theory. The correlations are nontrivial: they depend on the perturbation directions relative to the orientation of all object-pairs, simultaneously. Hence, we develop a theory characterizing and rendering such a distortion field. The theory is turned to a few simple 2D operations, which render images based on camera and atmospheric properties.
UR - http://www.scopus.com/inward/record.url?scp=85025467070&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ICCPHOT.2017.7951490
DO - https://doi.org/10.1109/ICCPHOT.2017.7951490
M3 - منشور من مؤتمر
T3 - 2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings
BT - 2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings
T2 - 2017 IEEE International Conference on Computational Photography, ICCP 2017
Y2 - 12 May 2017 through 14 May 2017
ER -