强度传输方程和神经网络融合的三维重构算法

针对原有强度传输方程法所恢复的相位精度不够精确的缺点，提出强度传输方程和神经网络融合的三维重构算法. 利用强度传输方程求解出物体不同角度的初始相位，利用神经网络算法进行优化，根据不同角度的最终恢复相位结合乘法技术重构出三维体信息. 该算法具有精度高的特点，可以为三维成像技术的应用提供参考. 对于实验中的示例图像，该算法将强度传输方程得到的相位误差从21.40%降低为5.26%，重构三维物体与模拟真实物体的相关程度为显著相关.

Three-dimensional reconstruction algorithm based on fusion of transport of intensity equation and neural network

A three-dimensional reconstruction algorithm based on the fusion of transport of intensity equation and neural network was proposed in order to improve the accuracy of the phase retrieved by the original transport of intensity equation method. The initial phases of different angles of the object were solved by transport of intensity equation and optimized by the neural network algorithm. Then the three-dimensional information was reconstructed according to the final retrieval phases with different angles and the multiplicative technique. The algorithm has the characteristics of high precision, and can provide reference for the application of three-dimensional imaging technology. The phase error obtained by the transport of intensity equation was reduced from 21.40% to 5.26% for the example image in the experiment. The correlation degree between the reconstructed three-dimensional object and the simulated object was significant.