English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Traditional snapshot hyperspectral imaging systems generally require multiplerefractive-optics-based elements to modulate light, resulting in bulkyframework. In pursuit of a more compact form factor, a metasurface-basedsnapshot hyperspectral imaging system, which achieves joint optimization ofmetasurface and image processing, is proposed in this paper. The unprecedentedlight manipulation capabilities of metasurfaces are used in conjunction withneural networks to encode and decode light fields for better hyperspectralimaging. Specifically, the extremely strong dispersion of metasurfaces isexploited to distinguish spectral information, and a neural network based onspectral priors is applied for hyperspectral image reconstruction. Byconstructing a fully differentiable model of metasurface-based hyperspectralimaging, the front-end metasurface phase distribution and the back-end recoverynetwork parameters can be jointly optimized. This method achieves high-qualityhyperspectral reconstruction results numerically, outperforming separationoptimization methods. The proposed system holds great potential forminiaturization and portability of hyperspectral imaging systems.

End-to-end joint optimization of metasurface and image processing for compact snapshot hyperspectral imaging