Spatial-Aware Modulation for Implicit Neural Representations

Abstract

Implicit Neural Representations (INRs) provide a flexible and resolution-independent formulation for continuous signal representation. Despite their strong representation ability, standard INRs usually predict each queried coordinate independently, making it difficult to explicitly exploit the local coherence widely observed in natural signals. For images and volumetric data, neighboring locations often share correlated responses in smooth regions, while sharp variations mainly appear around spatial transitions. Ignoring such local dependency may reduce learning efficiency and weaken the reconstruction of spatially consistent details. To address this limitation, we propose Spatial-Aware Implicit Neural Representation (SA-INR), which enhances INRs by introducing local feature aggregation into the hidden representation space. Motivated by local feature coherence, SA-INR aggregates neighboring coordinate features through a learnable spatial-aware local operator. The aggregation weights are initialized as a uniform mean filter, providing a smooth local bias during early optimization. As training proceeds, the aggregation weights are updated by reconstruction supervision and become adaptive to spatial content. To preserve coordinate-specific information and avoid over-smoothing, the aggregated feature is further integrated with the original feature through a residual connection. Extensive experiments on image representation, CT reconstruction, and image denoising demonstrate that SA-INR consistently improves reconstruction fidelity across different INR backbones and reconstruction tasks. These results suggest that explicitly modeling local feature interaction is an effective way to enhance continuous signal representation.