Abstract By illuminating targets with a modulated light signal, a time-of-flight (ToF) camera can calculate depth maps based on the phase shifts generated by the round-trip travel of the light from the targets back to the sensor. The accuracy of such depth imaging depends on the quality of the returned light. However, the calculation can be disturbed by various factors, including the external environment and the internal structure of the camera. Multiple coupled interferences can introduce noise into the depth data collected by a ToF camera in the spatial domain. It is difficult to express the relationship between the noise in depth maps and these mixed disturbances in a mathematical model. Based on the theory of differential entropy and a large amount of depth data from a ToF camera, this paper analyzes the characteristics of depth imaging entropy, proposes an evaluation method for depth image quality, and presents a multilayer perceptron model with information entropy (E-MLP) trained to optimize the accuracy of depth imaging. Experimental results show that this method can significantly improve the depth accuracy in the case of mixed noise.

中文翻译：

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耦合噪声ToF相机的深度精度和熵特性研究

摘要 通过用调制光信号照射目标，飞行时间 (ToF) 相机可以根据光从目标返回传感器的往返行程产生的相移计算深度图。这种深度成像的准确性取决于返回光的质量。但是，计算可能会受到各种因素的干扰，包括外部环境和相机的内部结构。多重耦合干扰可能会将噪声引入 ToF 相机在空间域中收集的深度数据中。很难用数学模型表达深度图中的噪声与这些混合扰动之间的关系。基于微分熵理论和来自 ToF 相机的大量深度数据，本文分析了深度成像熵的特点，提出了一种深度图像质量的评价方法，并提出了一种具有信息熵（E-MLP）训练的多层感知器模型，以优化深度成像的精度。实验结果表明，该方法可以显着提高混合噪声情况下的深度精度。