Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform
Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan
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.