Study of the depth accuracy and entropy characteristics of a ToF camera with coupled noise

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.