The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 23, 2022

Filed:

Jan. 20, 2021
Applicants:

Purdue Research Foundation, West Lafayette, IN (US);

High Performance Imaging Llc, West Lafayette, IN (US);

Inventors:

Charles Addison Bouman, West Lafayette, IN (US);

Sherman Jordan Kisner, Niskayuna, NY (US);

Assignee:

Purdue Research Foundation, West Lafayette, IN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06T 7/00 (2017.01); G03H 1/04 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G03H 1/0443 (2013.01); G06N 3/04 (2013.01); G06T 7/0002 (2013.01); G03H 2001/0445 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A computer-implemented method of latency reduction is disclosed for a digital holography optical imaging system. The method comprises receiving an incoming light field at a focal plane array of the digital holography optical imaging system, applying an interfering light field to the incoming light field at the focal plane array, and generating a holographic image based on the incoming light field and the interfering light field at the focal plane array. The method further comprises generating a sequence of phase errors based on the holographic image, generating at least one training parameter based on the sequence of phase errors, and training a neural network to revise the at least one training parameter using the sequence of phase errors, a time delay, and the at least one training parameter. The method further comprises predicting a future phase error for a future holographic image based on the revised training parameter.


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