Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials

Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials

The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system’s performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. There...

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Bibliographic Details
Main Authors: Fang Yuan, Yang Sun, Yuting Han, Hairong Chu, Tianxiang Ma, Honghai Shen
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Sensors
Subjects:
phase recovery
diffractive deep neural network
Zernike polynomial
Online Access:https://www.mdpi.com/1424-8220/24/2/698

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