Laboratory of Integrated Performance in Design (LIPID), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Translational Sensory & Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.
Sensors (Basel). 2022 Mar 19;22(6):2377. doi: 10.3390/s22062377.
Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverage a data-driven approach to learn optimal encoder-array sensitivities for a novel filter-array spectrometer. By taking advantage of the regularities mined from diverse online repositories, we are able to exploit low-dimensional patterns for improved spectral reconstruction from as few as p=2 channels. Furthermore, the performance of SpecRA is largely independent of signal complexity. Our results illustrate the superiority of our method over conventional approaches and provide a framework towards "fourth paradigm" spectral sensing. We hope that this work can help reduce the size, weight and cost constraints of future spectrometers for specific spectral monitoring tasks in applied contexts such as in remote sensing, healthcare, and quality control.
重建算法是可访问和紧凑数据采集的前沿。在本文中，我们提出了一种新的重建算法 SpecRA，它可以根据信号相对于先前观测的相对稀有性进行自适应调整。我们利用数据驱动的方法为新型滤波器阵列光谱仪学习最佳的编码器阵列灵敏度。通过利用从各种在线存储库中挖掘出的规律，我们能够利用低维模式来从少至 p=2 个通道改善光谱重建。此外，SpecRA 的性能在很大程度上独立于信号复杂度。我们的结果说明了我们的方法优于传统方法，并为“第四范式”光谱传感提供了一个框架。我们希望这项工作能够帮助减少未来光谱仪在特定光谱监测任务中的尺寸、重量和成本限制，例如在遥感、医疗保健和质量控制等应用领域。
