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- W4324339498 abstract "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." @default.
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- W4324339498 date "2023-03-15" @default.
- W4324339498 modified "2023-10-04" @default.
- W4324339498 title "Denoising diffusion networks applied to Mueller polarimetric images of brain tissue (Conference Presentation)" @default.
- W4324339498 doi "https://doi.org/10.1117/12.2649820" @default.
- W4324339498 hasPublicationYear "2023" @default.
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