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.

翻译：光谱成像沿着空间和光谱坐标收集并处理信息，这些信息以离散体素量化，可视为三维光谱数据立方体。光谱图像能够通过场景中物体、农作物和材料的光谱特性对其进行识别。由于大多数光谱光学系统仅能使用一维或最多二维传感器，直接从现有的商用传感器获取三维信息具有挑战性。作为替代方案，计算光谱成像作为一种传感工具应运而生，它利用二维编码投影获取三维数据，随后需通过计算重建过程恢复光谱图像。与传统的扫描系统相比，计算光谱成像能够开发快照式光学系统，从而缩短采集时间并降低计算存储成本。近年来，深度学习领域的进展使得设计数据驱动的计算光谱成像成为可能，以改善光谱图像重建，甚至直接从二维编码投影中完成高级任务，如分类、解混或异常检测。本文综述了计算光谱成像的进展，从光谱图像及其重要性出发，延续至最相关的压缩光谱光学系统；随后介绍结合深度学习的计算光谱成像，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。