Neural nano-optics for high-quality thin lens imaging.
Neural nano-optics for high-quality thin lens imaging.
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DOI：
10.1038/s41467-021-26443-0
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发表时间：
2021-11-29
影响因子：
16.6
通讯作者：
Heide F
中科院分区：
文献类型：
--
作者：
Tseng E;Colburn S;Whitehead J;Huang L;Baek SH;Majumdar A;Heide F
纳米光学成像仪可以在亚波长尺度上调制光，可以在从机器人到医学的不同领域实现新的应用。虽然超表面光学技术提供了一条通向超小型成像器的途径，但现有的方法获得的图像质量远远低于笨重的折射替代方法，从根本上受到大光圈和低f数像差的限制。在这项工作中，我们通过引入神经纳米光学成像器来缩小这一性能差距。我们设计了一个完全可微的学习框架，它结合基于神经特征的图像重建算法来学习亚表面的物理结构。通过实验验证，我们得到的重建误差比现有方法低一个数量级。因此，我们提出了一种高质量的纳米光学成像仪，它结合了全彩色超表面操作的最宽视场，同时在f数为2时实现了0.5 mm的最大演示孔径。尽管超光学技术有可能显著地使相机技术微型化，但捕获的图像质量仍然很差。共同设计了一个单一的元光学和软件校正，在这里，作者报告了质量与商业相机相当的全色成像。
Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2. While meta-optics have the potential to dramatically miniaturize camera technology, the quality of the captured images remains poor. Co-designing a single meta-optic and software correction, here the authors report on full-color imaging with quality comparable to commercial cameras.