  Hyperspectral cameras face challenging spatial-spectral resolution trade-offs
and are more affected by shot noise than RGB photos taken over the same total
exposure time. Here, we present a colorization algorithm to reconstruct
hyperspectral images from a grayscale guide image and spatially sparse spectral
clues. We demonstrate that our algorithm generalizes to varying spectral
dimensions for hyperspectral images, and show that colorizing in a low-rank
space reduces compute time and the impact of shot noise. To enhance robustness,
we incorporate guided sampling, edge-aware filtering, and dimensionality
estimation techniques. Our method surpasses previous algorithms in various
performance metrics, including SSIM, PSNR, GFC, and EMD, which we analyze as
metrics for characterizing hyperspectral image quality. Collectively, these
findings provide a promising avenue for overcoming the time-space-wavelength
resolution trade-off by reconstructing a dense hyperspectral image from samples
obtained by whisk or push broom scanners, as well as hybrid spatial-spectral
computational imaging systems.
