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11 March 2020 Alternation of inverse problem and deep learning approaches for phase unwrapping in lens-free microscopy (Conference Presentation)
Cédric Allier, Lionel Hervé, Dorothée Kraemer, Olivier Cioni, Mathilde Menneteau, Ondrej Mandula, Sophie Morales
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124914 (2020) https://doi.org/10.1117/12.2544812
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Lens-free microscopy aims at recovering sample image from diffraction measurements. The acquisitions are usually processed with an inverse problem approach. Recently, deep learning has been used to further improve phase retrieval results. Here, we propose to alternate iteratively between the two algorithms, to improve the reconstruction results without losing data fidelity. We validated this method for the phase image recovery of floating cells sample at large density acquired by means of lens-free microscopy. This is a challenging case with a lot of phase wrapping artefacts that has never been successfully solved using inverse problem approaches only.
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Cédric Allier, Lionel Hervé, Dorothée Kraemer, Olivier Cioni, Mathilde Menneteau, Ondrej Mandula, and Sophie Morales "Alternation of inverse problem and deep learning approaches for phase unwrapping in lens-free microscopy (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124914 (11 March 2020); https://doi.org/10.1117/12.2544812
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KEYWORDS
Inverse problems
Microscopy
Image restoration
Neural networks
Data processing
Diffraction
Image processing
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