  Spectral imaging extends the concept of traditional color cameras to capture
images across multiple spectral channels and has broad application prospects.
Conventional spectral cameras based on scanning methods suffer from low
acquisition speed and large volume. On-chip computational spectral imaging
based on metasurface filters provides a promising scheme for portable
applications, but endures long computation time for point-by-point iterative
spectral reconstruction and mosaic effect in the reconstructed spectral images.
In this study, we demonstrated on-chip rapid spectral imaging eliminating the
mosaic effect in the spectral image by deep-learning-based spectral data cube
reconstruction. We experimentally achieved four orders of magnitude speed
improvement than iterative spectral reconstruction and high fidelity of
spectral reconstruction over 99% for a standard color board. In particular, we
demonstrated video-rate spectral imaging for moving objects and outdoor driving
scenes with good performance for recognizing metamerism, where the concolorous
sky and white cars can be distinguished via their spectra, showing great
potential for autonomous driving and other practical applications in the field
of intelligent perception.
