计算光谱成像：当代综述
Computational Spectral Imaging: A Contemporary Overview
AI总结：
本文综述了计算光谱成像（CSI）的研究进展，重点介绍其通过二维编码投影获取三维光谱数据、结合深度学习实现重建与分类等高级任务的原理与方法。
摘要：
光谱成像沿着空间和光谱坐标收集并处理以离散体素量化的信息，这些信息可以被视为一个三维光谱数据立方体。光谱图像（SIs）允许通过物体的光谱行为来识别场景中的物体、作物和材料。由于大多数光谱光学系统只能使用一维或最多二维传感器，因此直接从现有商用传感器获取三维信息具有挑战性。作为一种替代方案，计算光谱成像（CSI）已成为一种感知工具，可以通过二维编码投影获得三维数据。然后，必须采用计算恢复过程来重建光谱图像。CSI 使得快照式光学系统的发展成为可能，与传统的扫描系统相比，它减少了采集时间并提供了较低的计算存储成本。深度学习（DL）的最新进展使得数据驱动的 CSI 设计成为可能，以改善光谱图像重建，甚至直接从二维编码投影执行高级任务，如分类、解混或异常检测。本文总结了 CSI 的进展，首先介绍光谱图像及其重要性；接着介绍最相关的压缩光谱光学系统。然后，将介绍与深度学习结合的 CSI，以及将物理光学设计与计算深度学习算法相结合以解决高级任务的最新进展。
英文摘要：
Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow identifying objects, crops, and materials in the scene through their spectral behavior. Since most spectral optical systems can only employ 1D or maximum 2D sensors, it is challenging to directly acquire the 3D information from available commercial sensors. As an alternative, computational spectral imaging (CSI) has emerged as a sensing tool where the 3D data can be obtained using 2D encoded projections. Then, a computational recovery process must be employed to retrieve the SI. CSI enables the development of snapshot optical systems that reduce acquisition time and provide low computational storage costs compared to conventional scanning systems. Recent advances in deep learning (DL) have allowed the design of data-driven CSI to improve the SI reconstruction or, even more, perform high-level tasks such as classification, unmixing, or anomaly detection directly from 2D encoded projections. This work summarises the advances in CSI, starting with SI and its relevance; continuing with the most relevant compressive spectral optical systems. Then, CSI with DL will be introduced, and the recent advances in combining the physical optical design with computational DL algorithms to solve high-level tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
