Phase retrieval methods for adaptive optics correction with diffractive samples

Emily Finan; Tom D. Milster

doi:10.1117/12.2569011

20 August 2020 Phase retrieval methods for adaptive optics correction with diffractive samples

Emily Finan, Tom D. Milster

Proceedings Volume 11500, ODS 2020: Industrial Optical Devices and Systems; 1150008 (2020) https://doi.org/10.1117/12.2569011
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Extension of adaptive optics (AO) techniques to future data storage applications requires consideration for light reflected off a diffractive surface. Simulations and experiments are presented to study the efficacy of various wavefront reconstruction methods for examining diffractive samples. Image processing techniques are applied as an alternative to a Shack-Hartmann wavefront sensor. A modified Gerchberg reconstruction algorithm is used to gather wavefront data from multiple image acquisitions at different defocus positions. Furthermore, these multiple images are gathered simultaneously with a single acquisition using the concept of phase retrieval with complex diversity. A computergenerated hologram (CGH) is designed, fabricated, and implemented experimentally for single-shot AO correction.

Conference Presentation

Citation Download Citation

Emily Finan and Tom D. Milster "Phase retrieval methods for adaptive optics correction with diffractive samples", Proc. SPIE 11500, ODS 2020: Industrial Optical Devices and Systems, 1150008 (20 August 2020); https://doi.org/10.1117/12.2569011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available