  Neural fields have rapidly been adopted for representing 3D signals, but
their application to more classical 2D image-processing has been relatively
limited. In this paper, we consider one of the most important operations in
image processing: upsampling. In deep learning, learnable upsampling layers
have extensively been used for single image super-resolution. We propose to
parameterize upsampling kernels as neural fields. This parameterization leads
to a compact architecture that obtains a 40-fold reduction in the number of
parameters when compared with competing arbitrary-scale super-resolution
architectures. When upsampling images of size 256x256 we show that our
architecture is 2x-10x more efficient than competing arbitrary-scale
super-resolution architectures, and more efficient than sub-pixel convolutions
when instantiated to a single-scale model. In the general setting, these gains
grow polynomially with the square of the target scale. We validate our method
on standard benchmarks showing such efficiency gains can be achieved without
sacrifices in super-resolution performance.
