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

Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields