Today's commodity camera systems rely on compound optics to map light
originating from the scene to positions on the sensor where it gets recorded as
an image. To record images without optical aberrations, i.e., deviations from
Gauss' linear model of optics, typical lens systems introduce increasingly
complex stacks of optical elements which are responsible for the height of
existing commodity cameras. In this work, we investigate \emph{flat
nanophotonic computational cameras} as an alternative that employs an array of
skewed lenslets and a learned reconstruction approach. The optical array is
embedded on a metasurface that, at 700~nm height, is flat and sits on the
sensor cover glass at 2.5~mm focal distance from the sensor. To tackle the
highly chromatic response of a metasurface and design the array over the entire
sensor, we propose a differentiable optimization method that continuously
samples over the visible spectrum and factorizes the optical modulation for
different incident fields into individual lenses. We reconstruct a megapixel
image from our flat imager with a \emph{learned probabilistic reconstruction}
method that employs a generative diffusion model to sample an implicit prior.
To tackle \emph{scene-dependent aberrations in broadband}, we propose a method
for acquiring paired captured training data in varying illumination conditions.
We assess the proposed flat camera design in simulation and with an
experimental prototype, validating that the method is capable of recovering
images from diverse scenes in broadband with a single nanophotonic layer.

本文研究了替代传统复合光学的平面纳米光计算相机系统，该系统采用一组倾斜透镜和学习重建方法，能够在单纳米光子层上从多样化场景中恢复图像，避免了光学像差问题。