| Issue |
EPJ Web Conf.
Volume 266, 2022
EOS Annual Meeting (EOSAM 2022)
|
|
|---|---|---|
| Article Number | 04007 | |
| Number of page(s) | 2 | |
| Section | Topical Meeting (TOM) 4- Bio-Medical Optics | |
| DOI | https://doi.org/10.1051/epjconf/202226604007 | |
| Published online | 13 October 2022 | |
https://doi.org/10.1051/epjconf/202226604007
Physics-informed machine learning for microscopy
1 Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
2 D-TAILS srl, Rome, Italy
3 Crestoptics, S.p.A., Italy
4 Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185, Rome, Italy
* Corresponding author: author@e-mail.org
Published online: 13 October 2022
We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvements starting from low exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution which we included in the training loss function. Our approach surpasses previous algorithms performance for microscopy data, moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.
© The Authors, published by EDP Sciences
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