  We introduce a novel video-rate hyperspectral imager with high spatial, and
temporal resolutions. Our key hypothesis is that spectral profiles of pixels in
a super-pixel of an oversegmented image tend to be very similar. Hence, a
scene-adaptive spatial sampling of an hyperspectral scene, guided by its
super-pixel segmented image, is capable of obtaining high-quality
reconstructions. To achieve this, we acquire an RGB image of the scene, compute
its super-pixels, from which we generate a spatial mask of locations where we
measure high-resolution spectrum. The hyperspectral image is subsequently
estimated by fusing the RGB image and the spectral measurements using a
learnable guided filtering approach. Due to low computational complexity of the
superpixel estimation step, our setup can capture hyperspectral images of the
scenes with little overhead over traditional snapshot hyperspectral cameras,
but with significantly higher spatial and spectral resolutions. We validate the
proposed technique with extensive simulations as well as a lab prototype that
measures hyperspectral video at a spatial resolution of $600 \times 900$
pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving
a frame rate at $18$fps.
