让不可见变为可见：通过物理引导的复原实现高质量太赫兹层析成像
Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration
AI总结：
针对太赫兹层析成像因强吸水、低噪声容忍度及衍射限制导致的图像模糊失真问题，提出融合多视角多光谱特征的SARNet网络，结合自建太赫兹数据库验证了其在图像复原与3D层析重建中的有效性。
摘要：
太赫兹（THz）层析成像凭借其在物体探测与检测中的非侵入性、非破坏性、非电离性、材料分类能力及超快速特性，近年来受到广泛关注。然而，其强吸水特性与低噪声容忍度会导致重建的太赫兹图像出现不期望的模糊与失真，且受衍射限制的太赫兹信号极大制约了现有复原方法的性能。为解决该问题，我们提出一种新型多视角子空间注意力引导复原网络（Subspace-Attention-guided Restoration Network，SARNet），该网络融合太赫兹图像的多视角与多光谱特征，以实现高效图像复原与3D层析重建。具体而言，SARNet采用多尺度分支提取视角内的时空光谱振幅与相位特征，并通过共享子空间投影与自注意力引导实现特征融合，随后开展视角间融合，利用相邻视角间的冗余信息进一步提升单视角复原效果。实验中，我们搭建了覆盖0.1 THz至4 THz宽频率范围的太赫兹时域光谱（THz-TDS）系统，用于构建隐藏3D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3D太赫兹层析重建应用中验证了SARNet模型的有效性。
英文摘要：
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.
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