Dataset

DZNE-2026-01075

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Model: Pre-trained model weights for YADO (You Accurately Denoise real Observations) Denoiser and Renoising DM (ECCV 2026), v1

Rassmann, S.DZNE* ; Kügler, D.DZNE* ; Reuter, M.DZNE*

2026
Zenodo

This record in other databases:  

Please use a persistent id in citations: doi:10.5281/zenodo.21065203

Abstract: Model weights for YADO (You Accurately Denoise real Observations) Denoiser and Renoiser as introduced in the paper "Rethinking Real-World MRI Denoising: Learning from Physical Noise" accepted to ECCV 2026. See also the Project Page.The code and docs are at: github.com/Deep-MI/YADO, and the weights are mostly also compatible with YODA. The checkpoints each contain the weights (`ckpt/last.pth`) and the `config.yml`.

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Contributing Institute(s):

1. Artificial Intelligence in Medicine (AG Reuter)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Appears in the scientific report 2026

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Contribution to a conference proceedings Rassmann, S. (First author)DZNE* ; Kügler, D.DZNE* ; Brunheim, S.DZNE* ; Ehses, P.DZNE* ; Reuter, M. (Last author)DZNE*
Rethinking Real-World MRI Denoising: Learning from Physical Noise
2026The 19th European Conference on Computer Vision, ECCV, MalmöMalmö, Sweden, 10 Sep 2026 - 12 Sep 20262026-09-102026-09-12 - (2026)2026   Files BibTeX | EndNote: XML, Text | RIS

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