Digital holographic wrapped phase aberration compensation method based on deep learning

A digital holographic wrapped phase aberration compensation method based on deep learning, applied to a digital holographic optical measurement system characterized in that, the method comprises two stages comprising a network training stage and a holographic measurement stage, and the method is divided into the following steps:

• a. in the network training stage, comprising:
• automatically generating, by a computer, simulated wrapped phase map data, wherein the simulated wrapped phase map data are obtained by constructing a microstructure phase aberration distribution using Zernike polynomials and converting the microstructure phase aberration distribution into a wrapped phase;
• and
• inputting, by the computer, the simulated wrapped phase map data into a neural network model for training to obtain a trained neural network model, wherein the neural network model is a deep residual network adopting a ResNet50 architecture and is configured to process wrapped phase data with a value in a range of [−π, π];
• and
• b. in the holographic measurement stage, comprising:
• building a digital holographic optical apparatus to measure a microstructure sample used for holographic imaging, wherein the microstructure sample is a transmission type sample or a reflection-type sample;
• and
• processing, by the trained neural network model, an aberration compensation processing on wrapped phase data of the microstructure sample, and obtaining a three-dimensional profile distribution of the microstructure sample, wherein the aberration compensation processing comprises a pre-compensated step performed before phase unwrapping and a residual compensation step performed after phase unwrapping.