The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Phase recovery and holographic image reconstruction using deep learning in neural networks
Open accessAbstract
Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. In this study, we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts. This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram, requiring fewer measurements in addition to being computationally faster. We validated this method by reconstructing the phase and amplitude images of various samples, including blood and Pap smears and tissue sections. These results highlight that challenging problems in imaging science can be overcome through machine learning, providing new avenues to design powerful computational imaging systems.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529 · cited 2×
“Deep learning has recently been applied to solving inverse problems in imaging science such as in super-resolution 43,44 , acceleration of the image acquisition speed of computed tomography (CT) 45 , magnetic resonance imaging (MRI) 46 , photoacoustic tomography 47 and holography 48,49 .”
Cited by
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
Self-citationDeep learning in medical image registration: a survey
Haskins, G., Kruger, U., & Yan, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01060-x · Cited by 704
Deep learning for tomographic image reconstruction
Wang, G., Ye, J. C., & De Man, B. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00273-z · Cited by 531
Multimode optical fiber transmission with a deep learning network
Rahmani, B., Loterie, D., Konstantinou, G., Psaltis, D., & Moser, C. (2018) · Light: Science & Applications · doi:10.1038/s41377-018-0074-1 · Cited by 389
Reliable deep-learning-based phase imaging with uncertainty quantification
Xue, Y., Cheng, S., Li, Y., & Tian, L. (2019) · Optica · doi:10.1364/optica.6.000618 · Cited by 198
Metadata
- Authors
- Rivenson, Y., Zhang, Y., Günaydın, H., Teng, D., & Ozcan, A.
- Venue
- Light: Science & Applications
- Published
- 2017-10-13
- Volume
- 7
- Issue
- 2
- Pages
- 17141-17141
- ISSN
- 2047-7538 (ISSN-L)
- OpenAlex
- W2612688942
- Cited by
- 1,059
- References
- 4
- FWCI
- 43.70
- Type
- journal article
- Language
- en
- Open access
- gold
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