Increase the data acquisition rate of a ghost polarimetry system via deep learning

Application of ghost polarimetry is significantly limited due to the low data acquisition rate. We present the integration of deep learning into a ghost polarimetry to analyze the intensity correlation function and subsequent formation of improved patterns with a modified spectrum of spatial frequen...

Full description

Saved in:
Bibliographic Details
Published in2024 International Conference Laser Optics (ICLO) p. 234
Main Authors Shumigai, V.S., Moreva, P.E., Tuchin, V.S., Startseva, A.M., Nasedkin, B.A., Tcypkin, A.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2024
Subjects
Biology
Correlation
correlation function
Data acquisition
data acquisition rate
Deep learning
ghost polarimetry
Laser applications
Optics
Polarimetry
Online AccessGet full text

Cover

More Information
Summary:Application of ghost polarimetry is significantly limited due to the low data acquisition rate. We present the integration of deep learning into a ghost polarimetry to analyze the intensity correlation function and subsequent formation of improved patterns with a modified spectrum of spatial frequencies. Proposed modification makes ghost polarimetry more attractive for biological researches, where the object is often dynamic.
ISSN:2642-5580
DOI:10.1109/ICLO59702.2024.10624566