A Deep Generative Approach to Oversampling in Ptychography
- URL: http://arxiv.org/abs/2207.14392v1
- Date: Thu, 28 Jul 2022 22:02:01 GMT
- Title: A Deep Generative Approach to Oversampling in Ptychography
- Authors: Semih Barutcu, Aggelos K. Katsaggelos, Do\u{g}a G\"ursoy
- Abstract summary: A major drawback of ptychography is the long data acquisition time.
We propose complementing sparsely acquired or undersampled data with data sampled from a deep generative network.
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.
- Score: 9.658250977094562
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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