  Terahertz (THz) imaging is one of the hotspots in the field of optics, where
the depth information retrieval is a key factor to restore the
three-dimensional appearance of objects. Impressive results for depth
extraction in visible and infrared wave range have been demonstrated through
deep learning (DL). Among them, most DL methods are merely data-driven, lacking
relevant physical priors, which thus request for a large amount of experimental
data to train the DL models.However, large training data acquirement in the THz
domain is challenging due to the requirements of environmental and system
stability, as well as the time-consuming data acquisition process. To overcome
this limitation, this paper incorporates a complete physical model representing
the THz image formation process into traditional DL networks to retrieve the
depth information of objects. The most significant advantage is the ability to
use it without pre-training, thereby eliminating the need for tens of thousands
of labeled data. Through experiments validation, we demonstrate that by
providing diffraction patterns of planar objects with their upper and lower
halves individually masked, the proposed physics-informed neural network (NN)
can automatically optimize and, ultimately, reconstruct the depth of the object
through interaction between the NN and a physical model. The obtained results
represent the initial steps towards achieving fast holographic THz imaging
using reference-free beams and low-cost power detection.
