摘要
We propose a fusion terahertz deep learning computed tomography framework designed to precisely reconstruct object 3D geometric information from THz temporal-spatio-spectral signals acquired through a terahertz time-domain spectroscopy system. This Unet-based fusion framework utilizes multi-scale branches for extracting spatio-spectral features, which undergo processing through an element-wise filter adaptive convolutional layer, resulting in high-quality restoration of THz 3D images. Furthermore, the proposed framework offers high scalability and adjustability, allowing users to choose their processing signal domains and seamlessly integrate their own modified fusion network.