  Hyperspectral imaging has attracted significant attention to identify
spectral signatures for image classification and automated pattern recognition
in computer vision. State-of-the-art implementations of snapshot hyperspectral
imaging rely on bulky, non-integrated, and expensive optical elements,
including lenses, spectrometers, and filters. These macroscopic components do
not allow fast data processing for, e.g real-time and high-resolution videos.
This work introduces Hyplex, a new integrated architecture addressing the
limitations discussed above. Hyplex is a CMOS-compatible, fast hyperspectral
camera that replaces bulk optics with nanoscale metasurfaces inversely designed
through artificial intelligence. Hyplex does not require spectrometers but
makes use of conventional monochrome cameras, opening up the possibility for
real-time and high-resolution hyperspectral imaging at inexpensive costs.
Hyplex exploits a model-driven optimization, which connects the physical
metasurfaces layer with modern visual computing approaches based on end-to-end
training. We design and implement a prototype version of Hyplex and compare its
performance against the state-of-the-art for typical imaging tasks such as
spectral reconstruction and semantic segmentation. In all benchmarks, Hyplex
reports the smallest reconstruction error. We additionally present what is, to
the best of our knowledge, the largest publicly available labeled hyperspectral
dataset for semantic segmentation.
