Multi-resolution Enhancement for Full Spectrum Neural Representations

Abstract:Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (INRs) offer a promising solution by encoding signals through coordinate-based neural networks, serving as surrogates of data, with computational and storage requirements scaling with network complexity rather than data dimensionality. However, smaller INRs struggle to faithfully represent the multi-scale structures, high-frequency information, and fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, a theoretically-guided hierarchical INR framework that distributes modeling across resolution scales and enables improved representation capacity through a novel enhancement network to recover subtle details. This multi-scale architecture allows smaller networks to retain the full spectrum of information while preserving training efficiency and lowering storage cost. Evaluated on distinct raw experimental measurements across scales and complexities, WIEN-INR represents a practical step toward broader adoption of neural representations in scientific workflows, delivering compact, robust, and high-fidelity representations.

Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)

arXiv:2509.15494 [cs.LG]

(or arXiv:2509.15494v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2509.15494 Focus to learn more arXiv-issued DOI via DataCite

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