Ghost imaging at submillimeter waves: correlation and machine learning methods

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Abstract

We present experimental results on computational submillimeter-wave ghost imaging schemes. The schemes include a dispersive element introducing quasi-incoherent field patterns to the field of view and bucket detection of the back-reflected field across a significantly broad bandwidth. A single bucket detection without discrimination of the field of view into image pixels is used. The imaging experiments at 220-330 GHz with dispersive hologram show successful computational ghost imaging of a corner-cube reflector target at 600-mm distance. Two separate image-forming methods are compared: correlation and machine-learning. In the correlation method, the image is formed by integrating the predetermined quasi-incoherent field patterns weighted with the bucket detections. In the machine-learning method, high image quality can be achieved after non-trivial training campaigns. The great benefit of the correlation method is that, while the quasi-incoherent patterns need to be known, no a priori iterative training to the images is required. The experiments with the correlation method demonstrate resolving of the target at 600-mm distance.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume12535
ISBN (Electronic)978-1-5106-6184-4
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventRadar Sensor Technology - Orlando, United States
Duration: 1 May 20233 May 2023

Publication series

NameSPIE Conference Proceedings
PublisherSPIE
ISSN (Print)0277-786X

Conference

ConferenceRadar Sensor Technology
Country/TerritoryUnited States
CityOrlando
Period01/05/202303/05/2023

Keywords

  • correlation
  • Ghost imaging
  • machine learning
  • terahertz imaging

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