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Single metalenses are limited by their physical constraints, precluding themselves from achieving high numerical aperture across a wide visible spectral band in large-aperture applications. A hybrid system that integrates a metalens with a refractive lens can address this issue, yet previous designs lacked sufficient flexibility. Here, by reanalyzing the generalized Snell's law, we introduce a new paradigm for the hybrid metalens design based on differentiable ray tracing. Through joint optimization of the phase distribution of the metalens and refractive lens parameters, our system achieves achromatic performance within the broad spectral range of 440-700 nm, with an aperture of 1 cm and an f-number of 1.4. Owing to the differentiable nature of the proposed system, it can be seamlessly integrated as the optical front-end into any differentiable computational imaging system. Our system offers unprecedented opportunities for the advancement of metalenses in innovative optical design and computational imaging domains.

As the realm of spectral imaging applications extends its reach into the domains of mobile technology and augmented reality, the demands for compact yet high-fidelity systems become increasingly pronounced. Conventional methodologies, exemplified by coded aperture snapshot spectral imaging systems, are significantly limited by their cumbersome physical dimensions and form factors. To address this inherent challenge, diffractive optical elements (DOEs) have been repeatedly employed as a means to mitigate issues related to the bulky nature of these systems. Nonetheless, it's essential to note that the capabilities of DOEs primarily revolve around the modulation of the phase of light. Here, we introduce an end-to-end computational spectral imaging framework based on a polarization-multiplexed metalens. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high-fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial-spectral reconstruction performance, underscoring the efficacy of this system in practical, real-world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

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.