Fast phase diversity wavefront correction using a neural network

Abstract

A phase diversity wavefront correction system for use in a multiple aperture optical imaging system forms an in-focus image as a composite, focused image from the multiple apertures of the system and also forms an additional image which is deliberately made out of focus to a known extent. Taken together, the two images are processed to create one or more metrics, such as the power metric and sharpness metric. Neural networks are provided, each having an output corresponding to a parameter of an aperture of the imaging system, such as a piston position (axial displacement) or tip/tilt (angular displacement) of one telescope with respect to the others in the system. The neural networks each correspond to one parameter of a telescope or a combinations of parameters and are trained to identify a subset of elements within the metrics that, when input into the network, produce the best estimate of the piston or tip/tilt position relative to a reference telescope or an estimate of a combination of parameters, such as the average of a subset of telescopes. During active use of the system, metrics generated from the in-focus and out-of-focus images of the object scene and the trained neural networks are used to provide estimates of piston and/or tip/tilt positions which are in turn used to drive the pistons and/or tip/tilt controllers to correct for aberrant movement and keep the telescopes phased.

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