University of Limoges, CNRS, XLIM, UMR 7252, 87000, Limoges, France.
Centre for Wireless Innovation (CWI), Institute of Electronics, Communications and Information Technology (ECIT), School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen's University Belfast, Belfast, BT3 9DT, UK.
Sci Rep. 2021 Feb 11;11(1):3545. doi: 10.1038/s41598-021-83021-6.
Recent demonstrations have shown that frequency-diverse computational imaging systems can greatly simplify conventional architectures developed for imaging by transferring constraints into the digital layer. Here, in order to limit the latency and processing burden involved in image reconstruction, we propose to truncate insignificant principal components of the sensing matrix that links the measurements to the scene to be imaged. In contrast to recent work using principle component analysis to synthesize scene illuminations, our generic approach is fully unsupervised and is applied directly to the sensing matrix. We impose no restrictions on the type of imageable scene, no training data is required, and no actively reconfigurable radiating apertures are employed. This paper paves the way to the constitution of a new degree of freedom in image reconstructions, allowing one to place the performance emphasis either on image quality or latency and computational burden. The application of such relaxations will be essential for widespread deployment of computational microwave and millimeter wave imagers in scenarios such as security screening. We show in this specific context that it is possible to reduce both the processing time and memory consumption with a minor impact on the quality of the reconstructed images.
最近的演示表明，频率分集计算成像系统可以通过将约束转移到数字层，极大地简化传统的成像架构。在这里，为了限制图像重建所涉及的延迟和处理负担，我们建议截断将测量值与要成像的场景联系起来的传感矩阵的不重要主成分。与最近使用主成分分析来合成场景照明的工作相比，我们的通用方法是完全无监督的，并直接应用于传感矩阵。我们不对可成像场景的类型施加任何限制，不需要训练数据，也不使用主动可重构辐射孔径。本文为图像重建开辟了一个新的自由度，允许将性能重点放在图像质量或延迟和计算负担上。这种松弛的应用对于在安全检查等场景中广泛部署计算微波和毫米波成像仪至关重要。我们在这个特定的上下文中表明，有可能在重建图像质量的影响较小的情况下，同时减少处理时间和内存消耗。
