Noah Maul

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Lehrstuhl für Informatik 5 (Mustererkennung)
On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting (2023) Wagner F, Thies M, Pfaff L, Aust O, Pechmann S, Weidner D, Maul N, et al. Conference contribution Optimizing CT Scan Geometries With and Without Gradients (2023) Thies M, Wagner F, Maul N, Pfaff L, Schneider LS, Syben C, Maier A Conference contribution, Original article Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising (2023) Wagner F, Thies M, Pfaff L, Maul N, Pechmann S, Gu M, Utz J, et al. Conference contribution Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model (2023) Maul N, Zinn K, Wagner F, Thies M, Rohleder M, Pfaff L, Kowarschik M, et al. Conference contribution Robust multi-contrast MRI denoising using trainable bilateral filters without noise-free targets (2023) Pfaff L, Wagner F, Hoßbach J, Preuhs E, Maul N, Thies M, Denzinger F, et al. Conference contribution Trainable joint bilateral filters for enhanced prediction stability in low-dose CT (2022) Wagner F, Thies M, Denzinger F, Gu M, Patwari M, Ploner S, Maul N, et al. Journal article Learning-based occupational x-ray scatter estimation (2022) Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A Journal article
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