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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...
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| creator | Shumigai, V.S. Moreva, P.E. Tuchin, V.S. Startseva, A.M. Nasedkin, B.A. Tcypkin, A.N. |
| description | 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. |
| doi_str_mv | 10.1109/ICLO59702.2024.10624566 |
| format | conference_proceeding |
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| ispartof | 2024 International Conference Laser Optics (ICLO), 2024, p.234-234 |
| issn | 2642-5580 |
| language | eng |
| recordid | cdi_ieee_primary_10624566 |
| source | IEEE Xplore All Conference Series |
| subjects | Biology Correlation correlation function Data acquisition data acquisition rate Deep learning ghost polarimetry Laser applications Optics Polarimetry |
| title | Increase the data acquisition rate of a ghost polarimetry system via deep learning |
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