Coherent L2S THz Imaging Systems

在电磁光谱的长波区域，如毫米波和太赫兹频率范围内，相干成像的性能不断提高，为这种传感器技术的广泛采用开辟了道路。考虑到这种波长在光学上无法触及的情况下成像和感测的能力，应用潜力是巨大的，例如任意环境条件下的遥感、地下成像无损测试、弹性自动汽车视觉系统或与安全相关的成像系统，如机场检查站的隐藏爆炸物检测。在所有这样的应用场景中，相干成像具有获得光学成像系统无法访问的信息的巨大优势，并且基本上能够直接从相位数据获得3D对象和场景信息。然而，获得这样的优势是以与所有相干照明和成像系统相关的基本成本为代价的，即必须处理诸如振铃、斑点和多径干扰之类的干扰伪影，这些干扰伪影可以完全抑制成像，并且在该频率范围内特别强。因此，考虑到这样的基本限制，这类系统的应用吸收仍然难以捉摸。该项目计划使用基于人工智能的方法来学习处理相干成像系统的这种基本限制，并培训和验证其在毫米波和太赫兹频率范围内的充分性，在这些频率范围内，系统参数的可变性对于图像生成过程具有特别大的自由度。沿着L2的思想，沿着完整的系统实现和图像分析流水线建立真正的端到端学习范例，预见到以下目标：-学习如何使用包含网络体系结构的物理知识来开发自适应合成图像生成方法，该方法增强图像质量并校正相干3D成像的依赖于场景的干扰伪影。-评估和理解基于机器学习的重建3D THz成像数据分割的稳健性，该分割源自稀疏照明和传感器布置，包括差分成像模式。-评估和学习是否可以直接从原始感官数据获得分割，而不需要通过合成重建的中间3D图像生成步骤。-学习依赖感官任务的系统适应如何最大限度地提高成像和识别能力，同时最大限度地减少硬件和数据采集工作。这些目标将通过使用基于MIMO(多输入多输出)FMCW(调频连续波)合成图像重建方法的THz成像系统进行实验解决，以允许系统配置和多照明信号的最大可变性，并作为合作伙伴开发的L2S方法的测试场景。

The increasing performance of coherent imaging in the long wavelength region of the electromagnetic spectrum, like the mm-wave and THz frequency ranges, has opened-up the path to a wide uptake of such sensor technologies. Application potential is enormous, given the capability of such wavelengths to image and sense in optically inaccessible situations, like inter alia remote sensing in arbitrary environmental conditions, subsurface imaging non-destructive testing, resilient autonomous car vision systems, or security related imaging systems like hidden explosives detection at airport checkpoints. In all such application scenarios coherent imaging has the immense advantage to attain information not accessible by optical imaging systems, and to be fundamentally capable to derive 3D object and scene information directly from phase data. However, such advantages are attained at the fundamental cost associated with all coherent illumination and imaging systems of having to cope with interference artifacts like ringing, speckles and multi-path interferences, which can totally inhibit image formation, and which are particularly strong in this frequency range. Application uptake for such systems remains elusive, therefore, given such fundamental restrictions. This project plans to use artificial intelligence-based approaches to learn to cope with such fundamental limitations of coherent imaging systems, and to train and validate their adequacy in the mm-wave and THz frequency ranges, where the system parameter variability has a particularly large degree of freedom for the image generation process. Along the idea of L2S to establish a true end-to-end learning paradigm along the complete system realization and image analysis pipeline, following goals are foreseen:- Learning how physical knowledge containing network architectures can be used to develop adaptive synthetic image generation approaches that enhance the image quality and correct scene dependent interference artifacts for coherent 3D imaging.- Evaluating and understanding the robustness of machine-learning based segmentation of reconstructed 3D THz imaging data originating from sparse illumination and sensor arrangements, including differential imaging modes.- Assessing and learning if segmentation can be attained directly from raw sensory data, without the intermediate 3D image generation step by synthetic reconstruction. - Learning how a sensory task dependent system adaptation can maximize imaging and recognition capabilities, and at the same time minimize hardware and data acquisition effort.These goals will be experimentally addressed using THz imaging systems based on MIMO (multiple-input multiple-output) FMCW (frequency-modulated continuous-wave) synthetic image reconstruction approaches, in order to allow a maximum variability of the system configuration and multiple-illumination signal variability and as a test scenario for the L2S methodologies developed by the partners.