Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform
Abstract
Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms. Recognizing the limitations of previous approaches, we introduce a new concept known as the phase-to-space (P2S) transform to significantly speed up the simulation. P2S is build upon three ideas: (1) reformulating the spatially varying convolution as a set of invariant convolutions with basis functions, (2) learning the basis function via the known turbulence statistics models, (3) implementing the P2S transform via a light-weight network that directly convert the phase representation to spatial representation. The new simulator offers 300x -- 1000x speed up compared to the mainstream split-step simulators while preserving the essential turbulence statistics.
Cite
@article{arxiv.2107.11627,
title = {Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform},
author = {Zhiyuan Mao and Nicholas Chimitt and Stanley H. Chan},
journal= {arXiv preprint arXiv:2107.11627},
year = {2021}
}
Comments
The paper will be published at the ICCV 2021