Snapshot Hyperspectral Imaging

Snapshot hyperspectral imaging aims to capture a full spectral and spatial image simultaneously, overcoming the limitations of traditional methods that require scanning or multiple exposures. Current research heavily focuses on improving reconstruction accuracy and speed through novel computational imaging techniques, including deep learning-based approaches like transformers and convolutional neural networks, often integrated with multi-camera systems or advanced optical elements such as metasurfaces. These advancements are driving applications in diverse fields, such as robotic perception, medical imaging (e.g., hemodynamic monitoring), environmental monitoring (e.g., plastic detection), and industrial inspection, by enabling real-time, high-resolution hyperspectral data acquisition.