Ghost imaging at submillimeter waves: correlation and machine learning methods

Aleksi Tamminen; Samu-Ville Pälli; Juha Ala-Laurinaho; Sazan Rexhepi; Zachary Taylor

doi:10.1117/12.2663776

14 June 2023 Ghost imaging at submillimeter waves: correlation and machine learning methods

Aleksi Tamminen, Samu-Ville Pälli, Juha Ala-Laurinaho, Sazan Rexhepi, Zachary Taylor

Proceedings Volume 12535, Radar Sensor Technology XXVII; 125350S (2023) https://doi.org/10.1117/12.2663776
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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 220-330GHz 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.

Conference Presentation

Citation Download Citation

Aleksi Tamminen, Samu-Ville Pälli, Juha Ala-Laurinaho, Sazan Rexhepi, and Zachary Taylor "Ghost imaging at submillimeter waves: correlation and machine learning methods", Proc. SPIE 12535, Radar Sensor Technology XXVII, 125350S (14 June 2023); https://doi.org/10.1117/12.2663776

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