i thought you were referring to this paper with the superresolution above:
they use anisotropic gaussian kernels (see fig. 2 for overview, fig. 6 for the pretty artifacts it produces, sec. 5.1.2 how to compute covariance… etc, it’s pretty clear i think).
for the reconstruction you want anisotropic because your CFA is incomplete. while incoming lighting would potentially result in something close to an isotropic blotch on a pixel after passing through a CFA lenslet, you don’t know whether the gaps you have in the blue channel actually just blur whatever you’ve seen at your sample points or whether there is an edge going through. so you fit gaussians to structure in the image and then use this for the interpolation.