Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform

Zhiyuan Mao; Nicholas Chimitt; Stanley H. Chan

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arXiv · 2107.11627

Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform

Zhiyuan Mao·Nicholas Chimitt·Stanley H. Chan

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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.

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eess.IV cs.CV physics.flu-dyn

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BibTeX RIS

Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan. 2021-07-24. Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform. https://arxiv.org/abs/2107.11627

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