Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform
Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan
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 built 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 converts the phase representation to spatial representation. The new simulator offers 300× – 1000× speed up compared to the mainstream split-step simulators while preserving the essential turbulence statistics.