Geology

Image resolution

Spectral imaging

Full spectral imaging

Noise (video)

Image (mathematics)

Filter (signal processing)

Encoding (memory)

Hyperspectral imaging

Artificial intelligence

Imaging spectroscopy

Computer science

Computer vision

Broadband

Remote sensing

Telecommunications

Light Science & Applications

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.

Deeply learned broadband encoding stochastic hyperspectral imaging