  Snapshot mosaic multispectral imagery acquires an undersampled data cube by
acquiring a single spectral measurement per spatial pixel. Sensors which
acquire $p$ frequencies, therefore, suffer from severe $1/p$ undersampling of
the full data cube. We show that the missing entries can be accurately imputed
using non-convex techniques from sparse approximation and matrix completion
initialised with traditional demosaicing algorithms. In particular, we observe
the peak signal-to-noise ratio can typically be improved by 2 to 5 dB over
current state-of-the-art methods when simulating a $p=16$ mosaic sensor
measuring both high and low altitude urban and rural scenes as well as
ground-based scenes.
