Deep learning-based digital holographic continuous phase noise reduction method for microstructure measurement

Abstract

A deep learning-based digital holographic continuous phase noise reduction method for microstructure measurement is provided. A MEMS microstructure is simulated to generate an object phase image through generation of random matrix superposition, noise in a digital holographic continuous phase map is simultaneously simulated to generate a noise grayscale image, and a simulation data set is thus created. An end-to-end convolutional neural network is designed, and a trained convolutional neural network is trained and obtained. A holographic interference pattern of an object under measurement is collected by photographing, and after spectrum extraction, angular spectrum diffraction, phase unwrapping, and distortion compensation, a continuous phase map containing only the object phase and noise is obtained and input into the trained convolutional neural network to obtain an object phase map. A simulation data set is accurately created in the disclosure, thereby the difficulty of collecting a large amount of experimental data is avoided.