The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Compressive fluorescence microscopy for biological and hyperspectral imaging
Open accessAbstract
The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices—especially in optics—have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy.
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Self-citationSparse MRI: The application of compressed sensing for rapid MR imaging
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Single-pixel imaging via compressive sampling
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The Fluorescent Toolbox for Assessing Protein Location and Function
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Cited by
Single-pixel infrared and visible microscope
Radwell, N., Mitchell, K. J., Gibson, G. M., Edgar, M. P., Bowman, R., & Padgett, M. J. (2014) · Optica · doi:10.1364/optica.1.000285 · Cited by 447
Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., et al. (2018) · Science · doi:10.1126/science.aan0096 · Cited by 257
Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring
Huang, Y., Beck, J. L., & Li, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12408 · Cited by 79
Metadata
- Authors
- Studer, V., Bobin, J., Chahid, M., Mousavi, H. S., Candes, E., & Dahan, M.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2012-06-11
- Volume
- 109
- Issue
- 26
- Pages
- E1679-87
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2114456237
- Cited by
- 411
- References
- 33
- FWCI
- 22.56
- Type
- journal article
- Language
- en
- Open access
- green
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