Deep-learning based on-chip rapid spectral imaging with high spatial resolution

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 the drawbacks of 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 due to point-by-point iterative spectral reconstruction and mosaic effect in the reconstructed spectral images. In this study, on-chip rapid spectral imaging was demonstrated, which eliminated the mosaic effect in the spectral image by deeplearning-based spectral data cube reconstruction. The experimental results show that 4 orders of magnitude faster than the iterative spectral reconstruction were achieved, and the fidelity of the spectral reconstruction for the standard color plate was over 99% for a standard strated for moving objects and outdoor driving scenes with good perwhite cars can be distinguished via their spectra, showing great pofield of intelligent perception.