Aberration correction method for Fourier ptychographic microscopy based on neural network

Jinlei Zhang; Xiao Tao; Chenning Tao; Peng Sun; Zhanghao Ding; Chang Wang; Zhenrong Zheng

doi:10.1117/12.2573583

10 October 2020 Aberration correction method for Fourier ptychographic microscopy based on neural network

Jinlei Zhang, Xiao Tao, Chenning Tao, Peng Sun, Zhanghao Ding, Chang Wang, Zhenrong Zheng

Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 1155005 (2020) https://doi.org/10.1117/12.2573583
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technology, which achieves high-resolution imaging with a wide filed-of-view by overcoming the limitation of the optical spatial-bandwidth-product (SBP). In the traditional FPM system, the aberration of the optical system is ignored, which may significantly degrade the reconstruction results. In this paper, we propose a novel FPM reconstruction method based on the forward neural network models with aberration correction, termed FNN-AC. Zernike polynomials are used to indicate the wavefront aberration in our method.Both the spectrum of the sample and coefficients of different Zernike modes are treated as the learnable weights in the trainable layers.By minimizing the loss function in the training process, the coefficients of different Zernike modes can be trained, which can be used to correct the aberration of the optical system. Simulation has been performed to verify the effectiveness of the FNN-AC.

Conference Presentation

Citation Download Citation

Jinlei Zhang, Xiao Tao, Chenning Tao, Peng Sun, Zhanghao Ding, Chang Wang, and Zhenrong Zheng "Aberration correction method for Fourier ptychographic microscopy based on neural network", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 1155005 (10 October 2020); https://doi.org/10.1117/12.2573583

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available