论文标题
快照干涉测量学3D成像通过压缩感和深度学习
Snapshot Interferometric 3D Imaging by Compressive Sensing and Deep Learning
论文作者
论文摘要
我们演示了基于干扰编码的单发压缩三维（3D）$（x，y，z）$成像。对象的深度尺寸被编码到光场的干涉光谱中，导致$（x，y，λ）$ datacube，随后通过单发光谱仪测量。通过实施高达$ 400 $的压缩比，我们可以从2D测量中重建$ 1G $ Voxels。从单个2D编码的测量中，开发了基于优化的压缩感测算法和深度学习网络的3D重建。由于快速获取速度，我们的方法能够以本机相机框架速率捕获体积活动，从而实现了动态场景的4D（体积时间）可视化。
We demonstrate single-shot compressive three-dimensional (3D) $(x, y, z)$ imaging based on interference coding. The depth dimension of the object is encoded into the interferometric spectra of the light field, resulting a $(x, y, λ)$ datacube which is subsequently measured by a single-shot spectrometer. By implementing a compression ratio up to $400$, we are able to reconstruct $1G$ voxels from a 2D measurement. Both an optimization based compressive sensing algorithm and a deep learning network are developed for 3D reconstruction from a single 2D coded measurement. Due to the fast acquisition speed, our approach is able to capture volumetric activities at native camera frame rates, enabling 4D (volumetric-temporal) visualization of dynamic scenes.
