Spatial-Spectral Trade-offs in Metasurface-Based Snapshot Hyperspectral Imaging
Abstract
Snapshot hyperspectral imaging maps a spatial--spectral datacube onto a two-dimensional detector, with recoverability depending on the datacube sampling regime, the scene prior, and the optical encoding. We study this co-dependence with algorithmic recovery and optical design optimization in a differentiable metasurface model by varying the spatial--spectral ratio and comparing average-coherence, mutual-information, and end-to-end objectives. For compressed imaging with three bands, optimization of single-parameter dielectric pillars produces lens-like designs with overlapping wavelength-dependent point-spread functions across the objectives tested. End-to-end reconstruction can mask this collapse on restricted training distributions, showing that reconstruction fidelity depends jointly on the optical encoding and scene prior. For simple nano-pillar meta-atom geometries, the optimized singular-value spectrum deteriorates as the datacube becomes more spectrally dominated, whereas a relaxed model with independent complex transmission at each wavelength reduces this dependence. These results identify restricted wavelength-dependent transmission control in the pillar library as an important contributor to the observed spectral collapse. This motivates an illustrative local super-pixel spectrometer model in which spectral encoding is studied separately from global image formation under a plane-wave illumination approximation.
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BibTeX
@misc{fitzpatrick2026spatialspectral,
title = {{Spatial-Spectral Trade-offs in Metasurface-Based Snapshot Hyperspectral Imaging}},
author = {Liam Fitzpatrick and Sean Molesky and Kai Wang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.28450},
eprint = {2609.28450},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28450},
}
RIS
TY - UNPB
TI - Spatial-Spectral Trade-offs in Metasurface-Based Snapshot Hyperspectral Imaging
AU - Liam Fitzpatrick
AU - Sean Molesky
AU - Kai Wang
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.28450
UR - https://arxiv.org/abs/2609.28450
AB - Snapshot hyperspectral imaging maps a spatial--spectral datacube onto a two-dimensional detector, with recoverability depending on the datacube sampling regime, the scene prior, and the optical encoding. We study this co-dependence with algorithmic recovery and optical design optimization in a differentiable metasurface model by varying the spatial--spectral ratio and comparing average-coherence, mutual-information, and end-to-end objectives. For compressed imaging with three bands, optimization of single-parameter dielectric pillars produces lens-like designs with overlapping wavelength-dependent point-spread functions across the objectives tested. End-to-end reconstruction can mask this collapse on restricted training distributions, showing that reconstruction fidelity depends jointly on the optical encoding and scene prior. For simple nano-pillar meta-atom geometries, the optimized singular-value spectrum deteriorates as the datacube becomes more spectrally dominated, whereas a relaxed model with independent complex transmission at each wavelength reduces this dependence. These results identify restricted wavelength-dependent transmission control in the pillar library as an important contributor to the observed spectral collapse. This motivates an illustrative local super-pixel spectrometer model in which spectral encoding is studied separately from global image formation under a plane-wave illumination approximation.
ER -