@inproceedings{Armin2017,
abstract = {Fourier ptychography is an imaging technique that overcomes the diffraction limit of conventional cameras with applications in microscopy and long range imaging. Diffraction blur causes resolution loss in both cases. In Fourier ptychography, a coherent light source illuminates an object, which is then imaged from multiple viewpoints. The reconstruction of the object from these set of recordings can be obtained by an iterative phase retrieval algorithm. However, the retrieval process is slow and does not work well under certain conditions. In this paper, we propose a new reconstruction algorithm that is based on convolutional neural networks and demonstrate its advantages in terms of speed and performance.},
author = {Kappeler, Armin and Ghosh, Sushobhan and Holloway, Jason and Cossairt, Oliver and Katsaggelos, Aggelos},
booktitle = {2017 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2017.8296574},
isbn = {978-1-5090-2175-8},
issn = {15224880},
keywords = {CNN,Convolutional Neural Network,Fourier ptychography},
month = {sep},
pages = {1712--1716},
publisher = {IEEE},
title = {{Ptychnet: CNN based fourier ptychography}},
url = {http://ieeexplore.ieee.org/document/8296574/},
volume = {2017-Septe},
year = {2017}
}

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