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10 October 2020 Aberration correction method for Fourier ptychographic microscopy based on neural network
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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.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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