Compressed sensing for terahertz body scanners

在过去的20年里，电磁波谱的太赫兹（THz）区域已经从曾经被称为太赫兹间隙发展成为一个非常有前途的应用领域。其中最突出的是安全成像，其中太赫兹全身扫描仪是一个生动的研究领域。对于此应用程序，高质量，实时或视频速率成像功能是必要的。然而，大规模的探测器阵列并不容易用于太赫兹辐射。这一挑战可以通过使用单个或几个非常敏感的探测器与机械扫描相结合来解决。因此，将高灵敏度、单像素探测器与快速、非机械扫描相结合的技术不仅对安全成像，而且对所有太赫兹成像应用都至关重要。由于在许多应用中太赫兹图像是相当光滑的，很可能它们在傅里叶和/或小波域中是稀疏的。事实上，最初的研究结果证明了这种稀疏性。利用压缩感知（CS）技术可以减少测量采集的数量，从而减少成像时间，因为如果只有一个像素可用，成像时间与采集的数量成正比。因此，CS是一种非常有前途的技术，可以显著改善太赫兹成像。该项目的目标是开发太赫兹人体扫描仪的测量设计和CS重建算法，以提高这些扫描仪的图像质量和成像速度。该算法将用于相敏被动太赫兹人体扫描仪和太赫兹雷达人体扫描仪。两者都是单像素相机，能够在5米的距离上成像。太赫兹光谱区域的特殊性需要在CS框架内解决几个挑战。例如，多路径传播、衍射问题、辐射的散斑和相干效应。在所有需要考虑的问题中，这些现象的精确建模是极其复杂的，因此需要在各个方面引入简化模型。这可能会导致严重的模型误差，即传感矩阵的误差。在项目期间，我们打算使用压缩反褶积方法来解决这些特定的挑战。此外，我们想用这两种扫描仪开发太赫兹成像过程的模型。将通过测量可访问的模型组件来验证模型。该项目是由柏林工业大学和慕尼黑工业大学的团队共同研究的。柏林工业大学贡献了其在太赫兹成像系统和CS在太赫兹成像中的应用方面的专业知识，而慕尼黑工业大学贡献了其在CS在雷达技术中的应用方面的专业知识。

Over the last 20 years the terahertz (THz) region of the electromagnetic spectrum has developed from once being known as the THz gap into a very promising region for many applications. The most prominent one is security imaging, where THz body scanners are a vivid research field. For this application high quality, real-time or video-rate imaging capabilities are necessary. However, large-scale detector arrays are not readily available for THz radiation. This challenge is addressed by using a single or just a few very sensitive detectors in combination with mechanical scanning. Therefore, techniques which combine high sensitivity, single pixel detectors with fast, non-mechanical scanning are crucial for not only for security imaging but for all THz imaging applications. Since in many applications THz images are quite smooth it is likely that they are sparse in the Fourier and/or wavelet domain. In fact, first research results demonstrate this sparsity. Utilizing Compressed Sensing (CS) techniques can reduce the number of measurement acquisitions and thus decreasing the imaging time, because if only a single pixel is available the imaging time is directly proportional to the number of acquisitions. Therefore CS is a very promising technique to improve THz imaging significantly. The objective of the project is to develop a measurement design and CS reconstruction algorithms for THz body scanners in order to improve the image quality and the imaging speed of these scanners. The algorithms will be developed for a phase-sensitive passive THz body scanner and a THz radar body scanner. Both are single pixel cameras and capable of imaging at 5 m stand-off distance. The peculiarities of the THz spectral region require tackling several challenges within the CS framework. These are, for example, multi-path propagation, diffraction problems, speckle and coherence effects of the radiation. Among all the issues to be account for, the exact modeling of these phenomena is extremely complex, and hence simplified models need to be introduced for diverse aspects. This may result in severe model errors, i.e. errors in the sensing matrix. During the project we intend to solve these specific challenges using a compressive deconvolution approach. Furthermore, we want to develop a model of the THz imaging process with both scanners. The model will be verified by measuring the accessible model components. The project is a joined research effort by teams of TU Berlin and TU München. TU Berlin contributes its expertise in THz imaging systems and application of CS to THz imaging while TU München contributes its expertise regarding the application of CS to radar techniques.