Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Utilizing Machine Learning for Smart Starting Guesses for Phase Retrieval

Not Accessible

Your library or personal account may give you access


Traditional wavefront-sensing phase retrieval problems with large amounts of wavefront error often do not converge without a good starting point. We use machine learning in an attempt to produce better starting guesses for these problems.

© 2017 Optical Society of America

PDF Article
More Like This
Broadband Phase Retrieval and Spectral Estimation with Multiple Unresolved Stars

Alden S. Jurling and James R. Fienup
FTu2F.3 Frontiers in Optics (FiO) 2012

Incorporating Polarization into Phase Retrieval Methods

Scott W. Paine and James R. Fienup
FM4C.5 Frontiers in Optics (FiO) 2018

Extending the Capture Range of Phase Retrieval through Random Starting Parameters

Dustin B. Moore and James R. Fienup
FTu2C.2 Frontiers in Optics (FiO) 2014


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription

Select as filters

Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved