SS-REPA: Spectral-Spatial Representation Alignment for Onboard Sparse Physical Field Reconstruction

Onboard sparse physical field reconstruction aims to recover dense physical states from a limited number of sensor measurements under tight memory and latency constraints. Compact convolutional encoder–decoders are attractive for deployment, but their capacity-constrained bottlenecks may struggle to capture the field-wide context required to infer unobserved regions. We propose Spectral-Spatial Representation Alignment (SS-REPA), a framework for improving bottleneck representations in compact convolutional encoder–decoders through alignment with a pretrained spectral neural operator. Specifically, SS-REPA introduces a dual-branch bottleneck with a mean-shift spatial branch and a spectral-context branch, providing separate pathways for local features reconstruction and global spectral interaction. Only the spectral-context branch features are mapped to the spectral neural operator feature space, where a Gaussian-weighted frequency-domain objective aligns their real and imaginary components while emphasizing global low-frequency structure. Extensive experiments on three sparse-sensor reconstruction benchmarks demonstrate that our method achieves competitive reconstruction accuracy with approximately 94% fewer parameters and 83.3–85.5% lower inference latency than the standard baseline on an edge computing device.