Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
arxiv(2023)
摘要
Recent approaches for arbitrary-scale single image super-resolution (ASSR)
have used local neural fields to represent continuous signals that can be
sampled at arbitrary rates. However, the point-wise query of the neural field
does not naturally match the point spread function (PSF) of a given pixel,
which may cause aliasing in the super-resolved image. We present a novel way to
design neural fields such that points can be queried with an adaptive Gaussian
PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
We achieve this with a novel activation function derived from Fourier theory.
Querying points with a Gaussian PSF, compliant with sampling theory, does not
incur any additional computational cost in our framework, unlike filtering in
the image domain. With its theoretically guaranteed anti-aliasing, our method
sets a new state of the art for ASSR, while being more parameter-efficient than
previous methods. Notably, even a minimal version of our model still
outperforms previous methods in most cases, while adding 2-4 orders of
magnitude fewer parameters. Code and pretrained models are available at
https://github.com/prs-eth/thera.
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