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Physics Augmented U-Net: A High-Frequency Aware Generative Prior for Microscopy

Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan, Andrew Seeber, View ORCID ProfileDushan N. Wadduwage
doi: https://doi.org/10.1101/2021.12.01.470743
Jathurshan Pradeepkumar
1Dept. of Electronic and Telecommunication Engineering, Univeristy of Moratuwa, Sri Lanka
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Mithunjha Anandakumar
1Dept. of Electronic and Telecommunication Engineering, Univeristy of Moratuwa, Sri Lanka
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Vinith Kugathasan
1Dept. of Electronic and Telecommunication Engineering, Univeristy of Moratuwa, Sri Lanka
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Andrew Seeber
2Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, USA
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Dushan N. Wadduwage
2Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, USA
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  • ORCID record for Dushan N. Wadduwage
  • For correspondence: wadduwage@fas.harvard.edu
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Abstract

A key challenge in optical microscopy is to image fast at high-resolution. To address this problem, we propose “Physics Augmented U-Net”, which combines deep learning and structured illumination microscopy (SIM). In SIM, the structured illumination aliases out-of-band high-frequencies to the passband of the microscope; thus SIM captures some high-frequencies even when the image is sampled at low-resolution. To utilize these features, we propose a three-element method: 1) a modified U-Net model, 2) a physics-based forward model of SIM 3) an inference algorithm combining the two models. The modified U-Net architecture is similar to the seminal work, but the bottleneck is modified by concatenating two latent vectors, one encoding low-frequencies (LFLV), and the other encoding high-frequencies (HFLV). LFLV is learned by U-Net contracting path, and HFLV is learned by a second encoding path. In the inference mode, the high-frequency encoder is removed; HFLV is then optimized to fit the measured microscopy images to the output of the forward model for the generated image by the U-Net. We validated our method on two different datasets under different experimental conditions. Since a latent vector is optimized instead of a 2D image, the inference mode is less computationally complex. The proposed model is also more stable compared to other generative prior-based methods. Finally, as the forward model is independent of the U-Net, Physics Augmented U-Net can enhance resolution on any variation of SIM without further retraining.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 02, 2021.
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Physics Augmented U-Net: A High-Frequency Aware Generative Prior for Microscopy
Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan, Andrew Seeber, Dushan N. Wadduwage
bioRxiv 2021.12.01.470743; doi: https://doi.org/10.1101/2021.12.01.470743
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Physics Augmented U-Net: A High-Frequency Aware Generative Prior for Microscopy
Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan, Andrew Seeber, Dushan N. Wadduwage
bioRxiv 2021.12.01.470743; doi: https://doi.org/10.1101/2021.12.01.470743

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