Abstract
The complete plenoptic function records radiance of rays from every location, at every angle, for every wavelength and at every time. The signal is multi-dimensional and has long relied on multi-modal sensing such as hybrid light field camera arrays. In this paper, we present a single camera hyperspectral light field imaging solution that we call Snapshot Plenoptic Imager (SPI). SPI uses spectral coded catadioptric mirror arrays for simultaneously acquiring the spatial, angular and spectral dimensions. We further apply a learning-based approach to improve the spectral resolution from very few measurements. Specifically, we demonstrate and then employ a new spectral sparsity prior that allows the hyperspectral profiles to be sparsely represented under a pre-trained dictionary. Comprehensive experiments on synthetic and real data show that our technique is effective, reliable, and accurate. In particular, we are able to produce the first wide FoV multi-spectral light field database.