CORDIS Project
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This project develops advanced learning methods for reconstructing images in fluorescence microscopy from incomplete and noisy data. By integrating physics-informed approaches with machine learning, it aims to enhance the accuracy and stability of imaging techniques, making them more reliable for biological research.
This project will develop model-aware, i.e. physics-informed, learning methods for solving imaging inverse problems (IIPs) in fluorescence microscopy imaging (FMI). IIPs are frequently encountered in FMI whenever a visual representation of a biological sample needs to be reconstructed from incomplete and noisy input measurements.
Such IIPs are typically ill-posed: their solution (if it exists) is unstable to perturbations.
Classical model-based approaches reformulate the IIP at hand as an energy…
UNIVERSITA DEGLI STUDI DI GENOVA
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