Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements

Autor: Dongdong Chen, Julian Tachella, Mike E. Davies

Publikováno v: Chen, D, Tachella, J & Davies, M E 2022, Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements . in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5637-5646, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022, New Orleans, Louisiana, United States, 19/06/22 . https://doi.org/10.1109/CVPR52688.2022.00556
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. However, most existing networks are trained with clean signals which are often hard or impossible to ob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab6ec4c3020a30354434cff595d6b378
https://hdl.handle.net/20.500.11820/9d5c06ac-52f5-466b-bce0-87bcf85a4c5a

Divergence prior and vessel-tree reconstruction

Autor: Marc Moreno Maza, Zhongwen Zhang, Maria Drangova, Egor Chesakov, Yuri Boykov, Dmitrii Marin

Publikováno v: Bone and Joint Institute
CVPR
Medical Biophysics Publications

© 2019 IEEE. We propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. As one important example of this general idea, we focus on vector fields modelling blood flow pattern

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72ce94acd5c737d182e43f2f7337886a
https://ir.lib.uwo.ca/boneandjointpub/358

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