  Terahertz tomographic imaging has recently arisen significant attention due
to its non-invasive, non-destructive, non-ionizing, material-classification,
and ultrafast-frame-rate nature for object exploration and inspection. However,
its strong water absorption nature and low noise tolerance lead to undesired
blurring and distortion of reconstructed terahertz images. Research groups aim
to deal with this issue through the use of synthetic data in the training
phase, but still, their performances are highly constrained by the
diffraction-limited terahertz signals. In this paper, we propose a novel
multi-scale spatio-spectral fusion Unet (MS3-Unet) that extracts multi-scale
features from the different spectral of terahertz image data for restoration.
MS3-Unet utilizes multi-scale branches to extract spatio-spectral features
which are then processed by element-wise adaptive filters, and then fused to
achieve high-quality terahertz image restoration. Here, we experimentally
construct ultra-high-speed terahertz time-domain spectroscopy system covering a
broad frequency range from 0.1 THz to 4 THz for building up
temporal/spectral/spatial/phase/material terahertz database of hidden 3-D
objects. Complementary to a quantitative evaluation, we demonstrate the
effectiveness of the proposed MS3-Unet image restoration approach on 3-D
terahertz tomographic reconstruction applications.
