  Implicit neural representations (INRs) have significantly advanced the field
of arbitrary-scale super-resolution (ASSR) of images. Most existing INR-based
ASSR networks first extract features from the given low-resolution image using
an encoder, and then render the super-resolved result via a multi-layer
perceptron decoder. Although these approaches have shown promising results,
their performance is constrained by the limited representation ability of
discrete latent codes in the encoded features. In this paper, we propose a
novel ASSR method named GaussianSR that overcomes this limitation through 2D
Gaussian Splatting (2DGS). Unlike traditional methods that treat pixels as
discrete points, GaussianSR represents each pixel as a continuous Gaussian
field. The encoded features are simultaneously refined and upsampled by
rendering the mutually stacked Gaussian fields. As a result, long-range
dependencies are established to enhance representation ability. In addition, a
classifier is developed to dynamically assign Gaussian kernels to all pixels to
further improve flexibility. All components of GaussianSR (i.e., encoder,
classifier, Gaussian kernels, and decoder) are jointly learned end-to-end.
Experiments demonstrate that GaussianSR achieves superior ASSR performance with
fewer parameters than existing methods while enjoying interpretable and
content-aware feature aggregations.
