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.

该论文提出了一种基于隐式图像函数的新网络设计——UltraSR，它深度融合了空间坐标和周期编码，并通过实验证明了空间编码对于高性能隐式图像函数的重要性，并在DIV2K基准测试中取得了新的最先进表现。

UltraSR：空间编码是隐式图像函数任意比例超分辨率的缺失关键