  Despite the demonstrated benefits of hyperspectral formalism for stem effect
corrections in the context of fiber dose measurements, this approach has not
been yet translated into volumetric measurements where cameras are typically
used for their distinguishing spatial resolution. This work investigates
demosaicing algorithms for polychromatic cameras based spectral imaging. The
scintillation and Cherenkov signals produced in a radioluminescent phantom are
imaged by a polychromatic camera and isolated using the spectral formalism. To
do so, five demosaicing algorithms are investigated from calibration to
measurements: a clustering method and four interpolation algorithms. The
resulting accuracy of scintillation and Cherenkov images is evaluated with
measurements of the differences (mean $\pm$ standard deviation) between the
obtained and expected signals from profiles drawn across a scintillation spot.
Signal-to-noise ratio and signal-to-background ratio are further measured and
compared in the resulting scintillation images. Clustering, OpenCV, bilinear,
Malvar and Menon demosaicing algorithms respectively yielded differences of
$3\pm6\%$, $0.1\pm0.5\%$, $0.5\pm0.5\%$, $1\pm3\%$ and $1\pm4\%$ in the
resulting scintillation images. For the Cherenkov images, all algorithms
provided differences below 1\%. All methods enabled measurements over the
detectability (SBR>2) and sensitivity (SNR>5) thresholds with the bilinear
algorithm providing the best SNR value. Hence, radioluminescent signals can
accurately be isolated using a single polychromatic camera. Moreover,
demosaicing using a bilinear kernel provided the best results and enabled stem
effect subtraction while preserving the full spatial resolution of the camera.
