  Hyperspectral imaging provides optical information with high-dimensional
spatial-temporal-spectral data cubes revealing intrinsic matter
characteristics. It has been widely applied in various intelligent inspection
applications. The existing hyperspectral imaging systems employ individual
optical prism or grating to separate different spectra, which require
complicated optical design that takes a heavy load for integration. The
emerging multispectral imaging sensor integrates narrow-band filters on each
pixel element, wasting most light throughput and trapped in the rigorous
tradeoff between spatial and spectral resolution. In this work, we report an
on-chip computational hyperspectral imaging framework with full spatial and
temporal resolution. By integrating different broadband filtering materials on
the imaging sensor, the target spectral information is non-uniformly and
intrinsically coupled on each pixel with high light throughput. Using
intelligent reconstruction algorithms, multi-channel images can be recovered
from each frame, realizing real-time hyperspectral imaging. Following such a
framework, we for the first time fabricated a broadband (400-1700 nm)
hyperspectral imaging sensor with an average light throughput of 71.8$\%$
(enabling 32.5 dB peak signal-to-noise ratio of spectral reconstruction on
ColorChecker Classic chart) and 89 wavelength channels (10 nm interval within
400-1000 nm and 25 nm interval within 1000-1700 nm). The average spectral
resolution achieves 2.65 nm at 400-1000 nm and 8.59 nm at 1000-1700 nm. The
demonstrated spatial resolution is 2048x2048 pixels at 47 fps, with $\sim$3.43
arc minute resolving ability at a 39-degree field of view. We employed the
prototype sensor to collect a large-scale hyperspectral image dataset (210
scenes over 8 categories), and demonstrated the sensor's wide application
potentials on a series of experiments ...
