  The recent success of NeRF and other related implicit neural representation
methods has opened a new path for continuous image representation, where pixel
values no longer need to be looked up from stored discrete 2D arrays but can be
inferred from neural network models on a continuous spatial domain. Although
the recent work LIIF has demonstrated that such novel approach can achieve good
performance on the arbitrary-scale super-resolution task, their upscaled images
frequently show structural distortion due to the faulty prediction on
high-frequency textures. In this work, we propose UltraSR, a simple yet
effective new network design based on implicit image functions in which spatial
coordinates and periodic encoding are deeply integrated with the implicit
neural representation. We show that spatial encoding is indeed a missing key
towards the next-stage high-accuracy implicit image function through extensive
experiments and ablation studies. Our UltraSR sets new state-of-the-art
performance on the DIV2K benchmark under all super-resolution scales comparing
to previous state-of-the-art methods. UltraSR also achieves superior
performance on other standard benchmark datasets in which it outperforms prior
works in almost all experiments. Our code will be released at
https://github.com/SHI-Labs/UltraSR-Arbitrary-Scale-Super-Resolution.
