scholarly journals Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts

2020 ◽  
Vol 28 (15) ◽  
pp. 21578 ◽  
Author(s):  
Iksung Kang ◽  
Fucai Zhang ◽  
George Barbastathis
Keyword(s):  
Neural Network ◽  
Phase Retrieval ◽  
Phase Extraction ◽  
Coherent Modulation

Low-Photon Holographic Phase Retrieval via a Deep Decoder Neural Network

2021 ◽  
Author(s):  
Hannah Lawrence ◽  
David A. Barmherzig ◽  
Michael Eickenberg ◽  
Marylou Gabrie
Keyword(s):  
Neural Network ◽  
Phase Retrieval

A biologically motivated neural network for phase extraction from complex sounds

2004 ◽  
Vol 90 (2) ◽  
pp. 98-104 ◽  
Author(s):  
Marcus Borst ◽  
Gerald Langner ◽  
G�nther Palm
Keyword(s):  
Neural Network ◽  
Phase Extraction ◽  
Complex Sounds

Spatial phase retrieval of vortex beam using convolutional neural network

2021 ◽  
Author(s):  
Ge Ding ◽  
Wenjie Xiong ◽  
Peipei Wang ◽  
Zebin Huang ◽  
Yanliang He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phase Retrieval ◽  
Phase Distribution ◽  
Spherical Wave ◽  
Vortex Beam ◽  
Phase Front ◽  
Free Space Optical ◽  
Practical Applications ◽  
Spatial Phase

Abstract Vortex beam (VB) possessing spatially helical phase–front has attracted widespread attention in free-space optical communication, etc. However, the spiral phase of VB is susceptible to atmospheric turbulence, and effective retrieval of the distorted conjugate phase is crucial for its practical applications. Herein, a convolutional neural network (CNN) approach to retrieve the phase distribution of VB is experimentally demonstrated. We adopt a spherical wave to interfere with VB for converting its phase information into intensity changes, and construct a CNN model with excellent image processing capabilities to directly extract phase–front features from the interferogram. Since the interference intensity is correlated with the phase–front, the CNN model can effectively reconstruct the wavefront of conjugate VB carrying different initial phases from a single interferogram. The results show that the CNN-based phase retrieval method has a loss of 0.1418 in the simulation and a loss of 0.2344 for the experimental data, and remains robust even in turbulence environments. This approach can improve the information acquisition capability for recovering the distorted wavefront and reducing the reliance on traditional inverse retrieval algorithms, which may provide a promising tool to retrieve the spatial phase distributions of VBs.

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