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:由于大多数光谱光学系统只能使用1D或最高2D传感器,直接从现有的商业传感器获取3D信息具有挑战性。作为一种替代办法,计算光谱成像(CSI)已经成为一种遥感工具,利用2D编码预测直接获得3D数据。然后,必须采用计算回收程序来检索SI。 CSI能够开发光学光学截图系统,缩短获取时间,提供较低的计算存储成本,与常规扫描系统相比,这种系统可以提供较低的计算存储成本。最近深层次学习(DL)的进展使得能够设计以数据驱动的CSI来改进SI的重建,甚至更难以从现有的商业传感器直接获取3D信息。作为一个替代工具,计算光谱成像(CSI)作为一个遥感工具,利用2D编码预测直接获取3D数据。然后,必须使用计算回收过程来检索SI。 CSI的进展,从SI开始,与SI及其高光谱化计算系统相结合。DL的最新设计进度将随着最新的光学水平、高光谱化、高光谱化的DL与高光谱化系统相结合。</s>