# Convolutional-Attentive Conditional Neural Process

Nano-IR hyperspectral imaging collects a spectrum point by point at every pixel, so a complete cube can take hours. CACNP feeds sparsely sampled spectra together with a few single-wavenumber structure images into a convolutional-attentive conditional neural process trained on synthetic cubes, returning the full cube and a per-voxel uncertainty. On experimental data of polystyrene and poly(methyl methacrylate) particles in epoxy, 5% of the spectra suffices, inference takes under 0.1 s, and reconstruction error approaches the sample noise level.

Nano-IR hyperspectral imaging collects a spectrum point by point at every pixel, so a complete cube can take hours. CACNP feeds sparsely sampled spectra together with a few single-wavenumber structure images into a convolutional-attentive conditional neural process trained on synthetic cubes, returning the full cube and a per-voxel uncertainty. On experimental data of polystyrene and poly(methyl methacrylate) particles in epoxy, 5% of the spectra suffices, inference takes under 0.1 s, and reconstruction error approaches the sample noise level.