Recent approaches to arbitrary-scale single image super-resolution (ASR) use neural fields to represent continuous signals that can be sampled at arbitrary resolutions. However, point-wise queries of neural fields do not naturally match the point spread function (PSF) of pixels, which may cause aliasing in the super-resolved image. Existing methods attempt to mitigate this by approximating an integral version of the field at each scaling factor, compromising both fidelity and generalization. In this work, we introduce neural heat fields, a novel neural field formulation that inherently models a physically exact PSF. Our formulation enables analytically correct anti-aliasing at any desired output resolution, and -- unlike supersampling -- at no additional cost. Building on this foundation, we propose Thera, an end-to-end ASR method that substantially outperforms existing approaches, while being more parameter-efficient and offering strong theoretical guarantees. The project page is at https://therasr.github.io.

翻译：近期的任意尺度单图像超分辨率方法采用神经场表示连续信号，可在任意分辨率下采样。然而，神经场的逐点查询与像素的点扩散函数并不自然匹配，可能导致超分辨率图像中出现混叠现象。现有方法试图通过在每个缩放因子下近似场的积分形式来缓解此问题，但这会同时损害保真度与泛化能力。本文提出神经热场，一种新颖的神经场表述，其本质建模了物理精确的点扩散函数。该表述能够在任意目标输出分辨率下实现解析正确的抗混叠处理，且与超采样不同，无需额外计算成本。基于此，我们提出Thera——一种端到端的任意尺度超分辨率方法，其在显著优于现有方法的同时，具有更高的参数效率并提供坚实的理论保证。项目页面位于 https://therasr.github.io。