  Obtaining high resolution images from low resolution data with clipped noise
is algorithmically challenging due to the ill-posed nature of the problem. So
far such problems have hardly been tackled, and the few existing approaches use
simplistic regularisers. We show the usefulness of two adaptive regularisers
based on anisotropic diffusion ideas: Apart from evaluating the classical
edge-enhancing anisotropic diffusion regulariser, we introduce a novel
non-local one with one-sided differences and superior performance. It is termed
sector diffusion. We combine it with all six variants of the classical
super-resolution observational model that arise from permutations of its three
operators for warping, blurring, and downsampling. Surprisingly, the evaluation
in a practically relevant noisy scenario produces a different ranking than the
one in the noise-free setting in our previous work (SSVM 2017).
