Kavli Affiliate: Ting Xu
| First 5 Authors: Qiangbo Zhang, Peicheng Lin, Chang Wang, Yang Zhang, Zeqing Yu
| Summary:
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.
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