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Mueller matrix coefficients are conventionally derived from averaged measurements of several polarimetric intensity images for each polarisation state.
However, averaging large numbers of measurements is not compatible with real-time surgical applications.
To overcome this limitation, we introduce a novel learning-based denoising framework aiming at recovering accurate, physically consistent and high signal-to-noise ratio (SNR) polarimetric scans from short-time noisy acquisitions.
We formulate a microstructure-aware denoising diffusion network and validate against current state-of-the-art denoising techniques for real images in healthy and diseased brain samples.
Ultimately, the performance is analysed for near-real-time applicability and the advantage of the proposed approach is discussed.
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Stefano Moriconi, Leonard A. Felger, Romain Gros, Ekkehard Hewer, Theoni Maragkou, Michael Murek, Tatiana Novikova, Omar Rodríguez-Núñez, Angelo Pierangelo, Philippe Schucht, Richard McKinley, "Denoising diffusion networks applied to Mueller polarimetric images of brain tissue (Conference Presentation)," Proc. SPIE PC12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023, PC123820H (15 March 2023); https://doi.org/10.1117/12.2649820