Ma Jingyue, Yu Zhenming, Cheng Liming, Di Jiayu, Zhan Ning, Zhou Yue, Zhao Haiying, Xu Kun
Opt Express. 2024 May 20;32(11):19684-19696. doi: 10.1364/OE.520788.
We propose, to the best of our knowledge, a novel deep learning-enabled four-dimensional spectral imaging system composed of a reflective coded aperture snapshot spectral imaging system and a panchromatic camera. The system simultaneously captures a compressively coded hyperspectral measurement and a panchromatic measurement. The hyperspectral data cube is recovered by the U-net-3D network. The depth information of the scene is then acquired by estimating a disparity map between the hyperspectral data cube and the panchromatic measurement through stereo matching. This disparity map is used to align the hyperspectral data cube and the panchromatic measurement. A designed fusion network is used to improve the spatial reconstruction of the hyperspectral data cube by fusing aligned panchromatic measurements. The hardware prototype of the proposed system demonstrates high-speed four-dimensional spectral imaging that allows for simultaneously acquiring depth and spectral images with an 8 nm spectral resolution between 450 and 700 nm, 2.5 mm depth accuracy, and a 1.83 s reconstruction time.
据我们所知，我们提出了一种新型的基于深度学习的四维光谱成像系统，该系统由一个反射编码孔径快照光谱成像系统和一个全色相机组成。该系统同时捕获压缩编码的高光谱测量值和全色测量值。高光谱数据立方体由U-net-3D网络恢复。然后，通过立体匹配估计高光谱数据立方体和全色测量值之间的视差图，获取场景的深度信息。该视差图用于对齐高光谱数据立方体和全色测量值。通过融合对齐的全色测量值，使用设计的融合网络来改善高光谱数据立方体的空间重建。所提出系统的硬件原型展示了高速四维光谱成像，能够在450至700nm之间以8nm的光谱分辨率、2.5mm的深度精度和1.83s的重建时间同时获取深度和光谱图像。
