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首页> 外文会议>AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing >Optimization of Compressive 4D-spatio-spectral Snapshot Imaging
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Optimization of Compressive 4D-spatio-spectral Snapshot Imaging

机译：压缩4D空间光谱快照成像的优化

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摘要

In this paper, a modified 3D computational reconstruction method in the compressive 4D-spectro-volumetric snapshot imaging system is proposed for better sensing spectral information of 3D objects. In the design of the imaging system, a microlens array (MLA) is used to obtain a set of multi-view elemental images (Els) of the 3D scenes. Then, these elemental images with one dimensional spectral information and different perspectives are captured by the coded aperture snapshot spectral imager (CASSI) which can sense the spectral data cube onto a compressive 2D measurement image. Finally, the depth images of 3D objects at arbitrary depths, like a focal stack, are computed by inversely mapping the elemental images according to geometrical optics. With the spectral estimation algorithm, the spectral information of 3D objects is also reconstructed. Using a shifted translation matrix, the contrast of the reconstruction result is further enhanced. Numerical simulation results verify the performance of the proposed method. The system can obtain both 3D spatial information and spectral data on 3D objects using only one single snapshot, which is valuable in the agricultural harvesting robots and other 3D dynamic scenes.
机译：为了更好地感知3D物体的光谱信息，本文提出了一种在压缩4D-光谱-体积快照成像系统中的改进的3D计算重建方法。在成像系统的设计中，微透镜阵列（MLA）用于获得3D场景的一组多视图基本图像（Els）。然后，这些带有一维光谱信息和不同视角的基本图像将被编码孔径快照光谱成像仪（CASSI）捕获，该成像仪可​​以将光谱数据立方体感应到压缩2D测量图像上。最后，通过根据几何光学反映射基本图像来计算任意深度（例如焦点堆栈）的3D对象的深度图像。使用光谱估计算法，还可以重建3D对象的光谱信息。使用移位平移矩阵，可以进一步增强重建结果的对比度。数值仿真结果验证了该方法的性能。该系统仅使用一个快照即可获取3D对象的3D空间信息和光谱数据，这在农业收割机器人和其他3D动态场景中很有价值。

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