Fluorescence image deconvolution microscopy via generative adversarial learning (FluoGAN) Article Swipe
Mayeul Cachia
,
Vasiliki Stergiopoulou
,
Luca Calatroni
,
Sébastien Schaub
,
Laure Blanc-Féraud
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6420/acc889
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6420/acc889
We propose FluoGAN, an unsupervised hybrid approach combining the physical modelling of fluorescence microscopy timelapse acquisitions with a generative adversarial learning procedure for the problem of image deconvolution. Differently from standard approaches combining a least-square data term based on one (long-time exposure) image with sparsity-promoting regularisation terms, FluoGAN relies on a data term being the distributional distance between the fluctuating observed timelapse (short-time exposure images) and the generative model. Such distance is computed by adversarial training of two competing architectures: a physics-inspired generator simulating the fluctuating behaviour as a Poisson process of the observed images combined with blur and undersampling, and a standard convolutional discriminator network. FluoGAN is a fully unsupervised approach requiring only a fluctuating sequence of blurred, undersampled and noisy images of the sample of interest as input. It can be complemented with prior knowledge on the desired solution such as sparsity, non-negativity etc. After having described the main ideas behind FluoGAN, we formulate the corresponding optimisation problem and report several results on simulated and real phantoms used by microscopy engineers to quantitatively assess spatial resolution. The comparison of FluoGAN with state-of-the-art methodologies shows improved resolution, allowing for high-precision reconstructions of fine structures in challenging real Ostreopsis cf Ovata data. The FluoGAN code is available at: https://github.com/cmayeul/FluoGAN .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6420/acc889
- OA Status
- hybrid
- References
- 32
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4297415831Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6420/acc889Digital Object Identifier
- Title
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Fluorescence image deconvolution microscopy via generative adversarial learning (FluoGAN)Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-29Full publication date if available
- Authors
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Mayeul Cachia, Vasiliki Stergiopoulou, Luca Calatroni, Sébastien Schaub, Laure Blanc-FéraudList of authors in order
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https://doi.org/10.1088/1361-6420/acc889Publisher landing page
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1361-6420/acc889Direct OA link when available
- Concepts
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Deconvolution, Fluorescence, Generative grammar, Microscopy, Fluorescence microscope, Artificial intelligence, Adversarial system, Computer science, Image (mathematics), Computer vision, Chemistry, Optics, Physics, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | IOP Publishing |
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| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by-nc-nd |
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| primary_location.raw_source_name | Inverse Problems |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6420/acc889 |
| publication_date | 2023-03-29 |
| publication_year | 2023 |
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