@article{Semih2022a,
abstract = {Ptychography is a well-studied phase imaging method that makes non-invasive imaging possible at a nanometer scale. It has developed into a mainstream technique with various applications across a range of areas such as material science or the defense industry. One major drawback of ptychography is the long data acquisition time due to the high overlap requirement between adjacent illumination areas to achieve a reasonable reconstruction. Traditional approaches with reduced overlap between scanning areas result in reconstructions with artifacts. In this paper, we propose complementing sparsely acquired or undersampled data with data sampled from a deep generative network to satisfy the oversampling requirement in ptychography. Because the deep generative network is pre-trained and its output can be computed as we collect data, the experimental data and the time to acquire the data can be reduced. We validate the method by presenting the reconstruction quality compared to the previously proposed and traditional approaches and comment on the strengths and drawbacks of the proposed approach.},
archivePrefix = {arXiv},
arxivId = {2207.14392},
author = {Barutcu, Semih and Katsaggelos, Aggelos K. and G{\"{u}}rsoy, Doğa},
eprint = {2207.14392},
journal = {arXiv preprint arXiv:2207.14392},
month = {jul},
title = {{A Deep Generative Approach to Oversampling in Ptychography}},
url = {http://arxiv.org/abs/2207.14392},
year = {2022}
}

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