Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow-Reference-Cited by-同舟云学术

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Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow

Published:2018-06-25 Issue:7 Volume:9 Page:3306
ISSN:2156-7085
Container-title:Biomedical Optics Express
language:en
Short-container-title:Biomed. Opt. Express

Author:

Jiang Shaowei,Guo Kaikai,Liao Jun,Zheng Guoan

Funder

National Institutes of Health (NIH)

National Science Foundation (NSF)

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics,Biotechnology

Reference48 articles.

1. Wide-field, high-resolution Fourier ptychographic microscopy

2. Quantitative phase imaging via Fourier ptychographic microscopy

3. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope

4. Synthetic aperture superresolution with multiple off-axis holograms

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1. Influence of loss function and electron dose on ptychography of 2D materials using the Wirtinger flow;Micron;2024-10

2. Optimization of the FPM iterative process based on bright-field spectral overlap rate analysis;Optics Letters;2024-09-11

3. Fourier ptychographic reconstruction with denoising diffusion probabilistic models;Optics & Laser Technology;2024-09

4. Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy;Computers in Biology and Medicine;2024-09

5. Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Local Mixture Network;Sensors;2024-06-24

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