UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution
This work addresses image quality issues in super-resolution for applications like computer vision and graphics, representing an incremental improvement over prior methods.
The authors tackled the problem of structural distortion in arbitrary-scale super-resolution using implicit neural representations by proposing UltraSR, which integrates spatial coordinates and periodic encoding to improve high-frequency texture prediction, achieving new state-of-the-art performance on the DIV2K benchmark across all scales.
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 approaches can achieve good performance on the arbitrary-scale super-resolution task, their upscaled images frequently show structural distortion due to the inaccurate prediction of high-frequency textures. In this work, we propose UltraSR, a simple yet effective new network design based on implicit image functions in which we deeply integrated spatial coordinates and periodic encoding with the implicit neural representation. Through extensive experiments and ablation studies, we show that spatial encoding is a missing key toward the next-stage high-performing implicit image function. Our UltraSR sets new state-of-the-art performance on the DIV2K benchmark under all super-resolution scales compared 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.