  Terahertz (THz) tomographic imaging has recently attracted significant
attention thanks to its non-invasive, non-destructive, non-ionizing,
material-classification, and ultra-fast nature for object exploration and
inspection. However, its strong water absorption nature and low noise tolerance
lead to undesired blurs and distortions of reconstructed THz images. The
diffraction-limited THz signals highly constrain the performances of existing
restoration methods. To address the problem, we propose a novel multi-view
Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-view
and multi-spectral features of THz images for effective image restoration and
3D tomographic reconstruction. To this end, SARNet uses multi-scale branches to
extract intra-view spatio-spectral amplitude and phase features and fuse them
via shared subspace projection and self-attention guidance. We then perform
inter-view fusion to further improve the restoration of individual views by
leveraging the redundancies between neighboring views. Here, we experimentally
construct a THz time-domain spectroscopy (THz-TDS) system covering a broad
frequency range from 0.1 THz to 4 THz for building up a
temporal/spectral/spatial/ material THz database of hidden 3D objects.
Complementary to a quantitative evaluation, we demonstrate the effectiveness of
our SARNet model on 3D THz tomographic reconstruction applications.
