Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.

翻译：高光谱成像通过在空间和频率域同时采集数据，提供丰富的物理或生物学信息。然而，传统高光谱成像存在仪器笨重、数据采集速率慢以及时空光谱权衡等固有局限。本文引入高光谱学习用于快照式高光谱成像，其中从子区域采样得到的高光谱数据被整合到学习算法中，以恢复超立方体。高光谱学习利用了这一概念：照片不仅是图像，还包含详细的光谱信息。通过对高光谱数据进行少量采样，可实现光谱感知学习，从RGB图像中恢复超立方体。高光谱学习能够恢复超立方体中完整的光谱分辨率，其性能可媲美科学光谱仪的高光谱分辨率。此外，通过利用普通智能手机的低速视频录制（视频由多帧RGB图像组成的时间序列），高光谱学习还能实现超快动态成像。为展示其通用性，我们以血管发育实验模型为例，通过统计和深度学习方法提取血流动力学参数。随后，利用普通智能手机摄像头，以毫秒级的超快时间分辨率评估外周微循环的血流动力学。这种光谱感知学习方法与压缩感知类似，但其透明性使得学习算法能可靠地恢复超立方体并提取关键特征。这种基于学习的快照式高光谱成像方法具有高光谱和高时间分辨率，并消除了时空光谱权衡，同时简化了硬件要求，为多种机器学习技术的应用提供了潜力。