  Hyperspectral imaging is useful for applications ranging from medical
diagnostics to agricultural crop monitoring; however, traditional scanning
hyperspectral imagers are prohibitively slow and expensive for widespread
adoption. Snapshot techniques exist but are often confined to bulky benchtop
setups or have low spatio-spectral resolution. In this paper, we propose a
novel, compact, and inexpensive computational camera for snapshot hyperspectral
imaging. Our system consists of a tiled spectral filter array placed directly
on the image sensor and a diffuser placed close to the sensor. Each point in
the world maps to a unique pseudorandom pattern on the spectral filter array,
which encodes multiplexed spatio-spectral information. By solving a
sparsity-constrained inverse problem, we recover the hyperspectral volume with
sub-super-pixel resolution. Our hyperspectral imaging framework is flexible and
can be designed with contiguous or non-contiguous spectral filters that can be
chosen for a given application. We provide theory for system design,
demonstrate a prototype device, and present experimental results with high
spatio-spectral resolution.
