Abstract

A phase-diversity wave-front sensor has been developed and tested at the Lockheed Palo Alto Research Labs (LPARL). The sensor consists of two CCD-array focal planes that record the best-focus image of an adaptive imaging system and an image that is defocused. This information is used to generate an object-independent function that is the input to a LPARL-developed neural network algorithm known as the General Regression Neural Network (GRNN). The GRNN algorithm calculates the wave-front errors that are present in the adaptive optics system. A control algorithm uses the calculated values to correct the errors in the optical system. Simulation studies and closed-loop experimental results are presented.

© 1994 Optical Society of America

Noise propagation in wave-front sensing with phase diversity

Ludovic Meynadier, Vincent Michau, Marie-Thérèse Velluet, Jean-Marc Conan, Laurent M. Mugnier, and Gérard Rousset
Appl. Opt. 38(23) 4967-4979 (1999)

Iterative reconstruction method in phase-diversity imaging

Naoshi Baba, Hiroyuki Tomita, and Noriaki Miura
Appl. Opt. 33(20) 4428-4433 (1994)

Sensing wave-front amplitude and phase with phase diversity

Stuart M. Jefferies, Michael Lloyd-Hart, E. Keith Hege, and James Georges
Appl. Opt. 41(11) 2095-2102 (2002)

Practical issues in wave-front sensing by use of phase diversity

Jean J. Dolne, Richard J. Tansey, Katherine A. Black, Jana H. Deville, Philip R. Cunningham, Kenneth C. Widen, and Paul S. Idell
Appl. Opt. 42(26) 5284-5289 (2003)

High-order myopic coronagraphic phase diversity (COFFEE) for wave-front control in high-contrast imaging systems.

B. Paul, L. M. Mugnier, J.-F. Sauvage, K. Dohlen, and M. Ferrari
Opt. Express 21(26) 31751-31768 (2013)