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.

翻译：太赫兹（THz）层析成像因其非侵入、非破坏、非电离、材料识别以及超快速等特性，在物体探索与检测领域备受关注。然而，太赫兹强烈的吸水特性与低噪声容忍度导致重建图像出现不期望的模糊与畸变。受衍射限制的太赫兹信号严重制约了现有复原方法的性能。针对此问题，我们提出了一种新颖的多视角子空间注意力引导复原网络（SARNet），该网络融合太赫兹图像的多视角与多光谱特征，以实现有效的图像复原与三维层析重建。为此，SARNet采用多尺度分支提取视角内空谱幅度与相位特征，并通过共享子空间投影与自注意力引导进行融合。随后，我们执行视角间融合，利用相邻视角间的冗余信息进一步改善单个视角的复原效果。实验中，我们搭建了覆盖0.1 THz至4 THz宽频范围的太赫兹时域光谱（THz-TDS）系统，以构建隐藏三维物体的时间/光谱/空间/材料太赫兹数据库。除定量评估外，我们还在三维太赫兹层析重建应用上验证了SARNet模型的有效性。