ABSTRACT A single pixel in time of flight cameras receives multiple reflected light from different scene points, resulting in erroneous depth information. In this paper, based on the proposed sparse decomposition, a coarse and fine sparse decomposition based on compressed sensing is applied to multipath separation. The applied method uses a linear combination of multiple frequency signals to modulate the source. The measured vector obtained through finite random measurements is subjected to two sparse decompositions – rough separation and detailed positioning, and finally the minimum direct path depth is accurately recovered. Under the premise of the same number of measurements, calculation amount, and storage space, the accuracy of the coarse and fine sparse decomposition based on compressed sensing is improved by nearly an order of magnitude compared to the sparse decomposition without compressed sensing. Moreover, our method can basically achieve multi-path separation accuracy to the sub-millimeter level.

中文翻译：

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基于压缩感知的飞行时间相机粗细稀疏分解多径纠错算法设计

摘要 飞行时间相机中的单个像素接收来自不同场景点的多个反射光，从而导致错误的深度信息。在本文中，基于所提出的稀疏分解，将基于压缩感知的粗细稀疏分解应用于多径分离。所应用的方法使用多个频率信号的线性组合来调制源。对通过有限随机测量得到的测量向量进行粗分离和细定位两次稀疏分解，最终精确恢复最小直接路径深度。在测量次数、计算量、存储空间相同的前提下，与没有压缩感知的稀疏分解相比，基于压缩感知的粗细稀疏分解的精度提高了近一个数量级。而且，我们的方法基本上可以实现亚毫米级的多径分离精度。