A hypernetwork estimates parameters $\{\mathbf{b}_1, \mathbf{W}_2\}^{(i,j)}$ of pixel-wise, local neural heat fields. The phase shifts $\mathbf{b}_1$ operate on globally learned components, before thermal activations scale each component depending on their frequency and the desired scaling factor. The components are then linearly combined using coefficients $\mathbf{W}_2$, resulting in an appropriately-blurred, continuous local neural field. This field is then rasterized at the appropriate sampling rate (resolution) to yield a part of the final output image (red square). Unlike previous methods, correct anti-aliasing is guaranteed by design!
Due to its principled observation model, our method achieves state-of-the-art performance on a variety of super-resolution benchmarks.
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