  Despite its fruitful applications in remote sensing, image super-resolution
is troublesome to train and deploy as it handles different resolution
magnifications with separate models. Accordingly, we propose a
highly-applicable super-resolution framework called FunSR, which settles
different magnifications with a unified model by exploiting context interaction
within implicit function space. FunSR composes a functional representor, a
functional interactor, and a functional parser. Specifically, the representor
transforms the low-resolution image from Euclidean space to multi-scale
pixel-wise function maps; the interactor enables pixel-wise function expression
with global dependencies; and the parser, which is parameterized by the
interactor's output, converts the discrete coordinates with additional
attributes to RGB values. Extensive experimental results demonstrate that FunSR
reports state-of-the-art performance on both fixed-magnification and
continuous-magnification settings, meanwhile, it provides many friendly
applications thanks to its unified nature.
