CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume PC13021 > Article
Presentation
18 June 2024 Phase Retrieval for Optical System Characterization: Design, Performance Analysis, and Experimental Validation of a Convolutional Neural Network
Noé Hirschauer, Gaelle Lucas-Leclin
Author Affiliations +
Proceedings Volume PC13021, Optical Fabrication and Testing VIII; PC1302107 (2024) https://doi.org/10.1117/12.3017107
Event: SPIE Optical Systems Design, 2024, Strasbourg, France
Abstract
We introduce a Convolutional Neural Network (CNN) designed for precise wavefront retrieval from point-spread function (PSF) intensity images. Our ResNet18-based CNN infers the first 15 Zernike standard coefficients using three PSF measurements symmetrically positioned around the focal point. The CNN is trained on a dataset of 300,000 simulated PSF image sets containing 4th and 6th order aberrations, with wavefront amplitudes of up to 10 λ. We achieve an accuracy exceeding 99% for each individual Zernike coefficient, with uncertainties ≤ λ/30, as validated on a dataset of simulated PSF images. We evaluate the CNN's performance on experimental PSF measurements, and the predictions are compared to direct wavefront measurements from a Shack-Hartmann sensor. The results indicate prediction accuracy better than λ/15 for each of the 15 coefficients. This confirms the CNN's potential for characterizing optical systems with complex aberration distributions of low to moderate amplitudes.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Noé Hirschauer and Gaelle Lucas-Leclin "Phase Retrieval for Optical System Characterization: Design, Performance Analysis, and Experimental Validation of a Convolutional Neural Network", Proc. SPIE PC13021, Optical Fabrication and Testing VIII, PC1302107 (18 June 2024); https://doi.org/10.1117/12.3017107
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 PRESENTATION
WATCH
PRESENTATION SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
Point spread functions
Convolutional neural networks
Design and modelling
Optical networks
Phase retrieval
Education and training
CMOS sensors
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top