We present recent developments of a standoff imaging system based on a frequency-diverse phase hologram and deep neural networks. The single-pixel imaging system operates in a monostatic configuration consisting of a 340-GHz FMCW radar and a frequency-diverse phase hologram to interrogate the radar down range direction with spatially varying, frequency-dependent field patterns. The measured back-reflected signal contains spatial reflectivity information from the target, and the fast chirp rate of the radar enables real-time imaging performance. Together with simultaneously acquired visible-light images, a deep neural network integrated into the submillimeter-wave data readout electronics can map the received signal onto a 2D image without mechanical or active electrical beam scanning. In experiments, we have collected submillimeter-wave and visible-light data of a moving target in the region of interest with a 60-Hz frame rate. The results suggest that the system can image the moving target with a resolution comparable to the theoretical diffraction limit. The minimal hardware complexity and good imaging performance of the demonstrated computational submillimeter-wave imaging system support its potential as a cost-effective and easily deployable solution for various imaging applications.
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