公开(公告)号：US20240273691A1

公开(公告)日：2024-08-15

申请号：US18645399

申请日：2024-04-25

申请人： ZHEJIANG SCI-TECH UNIVERSITY

发明人： Benyong CHEN ,  Liu HUANG ,  Jianjun TANG ,  Liping YAN

IPC分类号： G06T5/80 ,  G03H1/04 ,  G06T3/18 ,  G06T5/20 ,  G06T7/136

CPC分类号： G06T5/80 ,  G03H1/0443 ,  G06T3/18 ,  G06T5/20 ,  G06T7/136 ,  G03H2226/02 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20192

摘要： In a digital holographic wrapped phase aberration compensation method based on deep learning, a random Zernike polynomial coefficient and a corresponding wrapped phase map are generated by a computer and are respectively treated as a learning label and a network to train a neural network model. A digital holographic optical setup is built to record a hologram of a sample to be measured, the wrapped phase map is inputted into the trained neural network model after numerical reconstruction, and the Zernike polynomial coefficient is outputted to reconstruct a phase aberration distribution and to compensate complex amplitude in a spatial domain. Phase filtering and unwrapping are performed on the compensated wrapped phase map, and Zernike polynomial fitting based on background segmentation is performed on the unwrapped phase to compensate for residual aberration.

公开(公告)号：US20240361727A1

公开(公告)日：2024-10-31

申请号：US18599084

申请日：2024-03-07

申请人： ZHEJIANG SCI-TECH UNIVERSITY

发明人： Benyong CHEN ,  Jianjun TANG ,  Liping YAN ,  Liu HUANG

IPC分类号： G03H1/08 ,  G01B11/25 ,  G03H1/16

CPC分类号： G03H1/0866 ,  G01B11/2527 ,  G03H1/16 ,  G03H2001/0875

摘要： 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.