  In this paper, we introduce a novel implicit neural network for the task of
single image super-resolution at arbitrary scale factors. To do this, we
represent an image as a decoding function that maps locations in the image
along with their associated features to their reciprocal pixel attributes.
Since the pixel locations are continuous in this representation, our method can
refer to any location in an image of varying resolution. To retrieve an image
of a particular resolution, we apply a decoding function to a grid of locations
each of which refers to the center of a pixel in the output image. In contrast
to other techniques, our dual interactive neural network decouples content and
positional features. As a result, we obtain a fully implicit representation of
the image that solves the super-resolution problem at (real-valued) elective
scales using a single model. We demonstrate the efficacy and flexibility of our
approach against the state of the art on publicly available benchmark datasets.
