  Traditional snapshot hyperspectral imaging systems generally require multiple
refractive-optics-based elements to modulate light, resulting in bulky
framework. In pursuit of a more compact form factor, a metasurface-based
snapshot hyperspectral imaging system, which achieves joint optimization of
metasurface and image processing, is proposed in this paper. The unprecedented
light manipulation capabilities of metasurfaces are used in conjunction with
neural networks to encode and decode light fields for better hyperspectral
imaging. Specifically, the extremely strong dispersion of metasurfaces is
exploited to distinguish spectral information, and a neural network based on
spectral priors is applied for hyperspectral image reconstruction. By
constructing a fully differentiable model of metasurface-based hyperspectral
imaging, the front-end metasurface phase distribution and the back-end recovery
network parameters can be jointly optimized. This method achieves high-quality
hyperspectral reconstruction results numerically, outperforming separation
optimization methods. The proposed system holds great potential for
miniaturization and portability of hyperspectral imaging systems.
