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[DZNE-2026-01059]
Contribution to a conference proceedings/Contribution to a book
Favaro, P. ; Kukelova, Z. ; Maki, A. ; et al
Rethinking Real-World MRI Denoising: Learning from Physical Noise
2026Computer Vision – ECCV 2026 / Favaro, Paolo (Editor) [https://orcid.org/0000-0003-3546-8247] ; Cham : Springer Nature Switzerland, 2026, Chapter 27 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-032-37434-9=978-3-032-37435-6 ; doi:10.1007/978-3-032-37435-6 European Conference on Computer Vision, ECCV, MalmöMalmö, Sweden, 8 Sep 2026 - 12 Sep 20262026-09-082026-09-12
Cham : Springer Nature Switzerland, Lecture Notes in Computer Science 17039, 498 - 517 (2026) [10.1007/978-3-032-37435-6_27]2026
Magnetic resonance imaging (MRI) inherently suffers from noise, which limits downstream medical analyses. In MRI, noise-free images are unobtainable; therefore, existing denoising approaches formulate surrogate training objectives, compromising between preserving detail and concealing noise, causing domain shifts or incomplete denoising. [...]
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