The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Open accessAbstract
Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixed medium. However, this "one-to-one" mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical "one-to-all" deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.
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Cites
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Statistical validation of image segmentation quality based on a spatial overlap index1
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Cited by
Phase imaging with an untrained neural network
Wang, F., Bian, Y., Wang, H., Lyu, M., Pedrini, G., Osten, W., Barbastathis, G., & Situ, G. (2020) · Light: Science & Applications · doi:10.1038/s41377-020-0302-3 · Cited by 503
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
Self-citationDeep learning: step forward to high‐resolution in vivo shortwave infrared imaging
Baulin, V. A., Usson, Y., & Le Guével, X. (2021) · Journal of Biophotonics · doi:10.1002/jbio.202100102 · Cited by 10
Metadata
- Authors
- Li, Y., Xue, Y., & Tian, L.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2018-09-25
- Volume
- 5
- Issue
- 10
- Pages
- 1181
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2808192168
- Cited by
- 527
- References
- 37
- FWCI
- 16.15
- Type
- journal article
- Language
- en
- Open access
- gold
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