High-speed holographic imaging using compressed sensing and phase retrieval. Wang, Z., Ryu, D., He, K., Horstmeyer, R., Katsaggelos, A., & Cossairt, O. In Mahalanobis, A., Ashok, A., Tian, L., Petruccelli, J. C., & Kubala, K. S., editors, Computational Imaging II, volume 10222, pages 102220G, may, 2017. ![High-speed holographic imaging using compressed sensing and phase retrieval [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Digital in-line holography serves as a useful encoder for spatial information. This allows three-dimensional reconstruction from a two-dimensional image. This is applicable to the tasks of fast motion capture, particle tracking etc. Sampling high resolution holograms yields a spatiotemporal tradeoff. We spatially subsample holograms to increase temporal resolution. We demonstrate this idea with two subsampling techniques, periodic and uniformly random sampling. The implementation includes an on-chip setup for periodic subsampling and a DMD (Digital Micromirror Device)-based setup for pixel-wise random subsampling. The on-chip setup enables direct increase of up to 20 in camera frame rate. Alternatively, the DMD-based setup encodes temporal information as high-speed mask patterns, and projects these masks within a single exposure (coded exposure). This way, the frame rate is improved to the level of the DMD with a temporal gain of 10. The reconstruction of sub sampled data using the aforementioned setups is achieved in two ways. We examine and compare two iterative reconstruction methods. One is an error reduction phase retrieval and the other is sparsity-based compressed sensing algorithm. Both methods show strong capability of reconstructing complex object fields. We present both simulations and real experiments. In the lab, we image and reconstruct structure and movement of static polystyrene microspheres, microscopic moving peranema, macroscopic fast moving fur and glitters.
@inproceedings{Zihao2017b,
abstract = {Digital in-line holography serves as a useful encoder for spatial information. This allows three-dimensional reconstruction from a two-dimensional image. This is applicable to the tasks of fast motion capture, particle tracking etc. Sampling high resolution holograms yields a spatiotemporal tradeoff. We spatially subsample holograms to increase temporal resolution. We demonstrate this idea with two subsampling techniques, periodic and uniformly random sampling. The implementation includes an on-chip setup for periodic subsampling and a DMD (Digital Micromirror Device)-based setup for pixel-wise random subsampling. The on-chip setup enables direct increase of up to 20 in camera frame rate. Alternatively, the DMD-based setup encodes temporal information as high-speed mask patterns, and projects these masks within a single exposure (coded exposure). This way, the frame rate is improved to the level of the DMD with a temporal gain of 10. The reconstruction of sub sampled data using the aforementioned setups is achieved in two ways. We examine and compare two iterative reconstruction methods. One is an error reduction phase retrieval and the other is sparsity-based compressed sensing algorithm. Both methods show strong capability of reconstructing complex object fields. We present both simulations and real experiments. In the lab, we image and reconstruct structure and movement of static polystyrene microspheres, microscopic moving peranema, macroscopic fast moving fur and glitters.},
author = {Wang, Zihao and Ryu, Donghun and He, Kuan and Horstmeyer, Roarke and Katsaggelos, Aggelos and Cossairt, Oliver},
booktitle = {Computational Imaging II},
doi = {10.1117/12.2262737},
editor = {Mahalanobis, Abhijit and Ashok, Amit and Tian, Lei and Petruccelli, Jonathan C. and Kubala, Kenneth S.},
isbn = {9781510609457},
issn = {1996756X},
month = {may},
pages = {102220G},
title = {{High-speed holographic imaging using compressed sensing and phase retrieval}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2262737},
volume = {10222},
year = {2017}
}
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