Learning low-dimensional models of microscopes

Abstract : We propose original, accurate and computationally efficient procedures to calibrate fluorescence microscopes from micro-beads images. The designed algorithms present many singularities. First, they allow to estimate space-varying blurs, which is a critical feature for large fields of views. Second, we propose a novel approach for calibration: instead of describing an optical system through a single operator, we suggest to vary the imaging conditions (temperature, focus, active elements) to get indirect images of its different states. Our algorithms then allow to represent the microscope responses as a low-dimensional convex set of operators. This novel approach is shown to significantly improve the estimation on a wide-field microscope. It is deemed as an essential step towards the effective resolution of blind inverse problems. We illustrate the potential of the approach by designing an original procedure for blind image deblurring of point sources and show a massive improvement compared to commercial software.
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Contributor : Valentin Debarnot <>
Submitted on : Monday, January 20, 2020 - 12:36:33 PM
Last modification on : Thursday, January 23, 2020 - 6:22:13 PM


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  • HAL Id : hal-02445642, version 1


Valentin Debarnot, Paul Escande, Thomas Mangeat, Pierre Weiss. Learning low-dimensional models of microscopes. 2020. ⟨hal-02445642⟩



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