Noémie Debroux
Orcid: 0000-0002-5329-5485
According to our database1,
Noémie Debroux
authored at least 19 papers
between 2015 and 2023.
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Bibliography
2023
CoRR, 2023
Asymptotic Result for a Decoupled Nonlinear Elasticity-Based Multiscale Registration Model.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023
2021
Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution.
Medical Image Anal., 2021
Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction.
Medical Image Anal., 2021
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021
2020
A Variational Model Dedicated to Joint Segmentation, Registration, and Atlas Generation for Shape Analysis.
SIAM J. Imaging Sci., 2020
A Nonlocal Laplacian-Based Model for Bituminous Surfacing Crack Recovery and its MPI Implementation.
J. Math. Imaging Vis., 2020
A Time-Dependent Joint Segmentation and Registration Model: Application to Longitudinal Registration of Hepatic DCE-MRI Sequences.
Proceedings of the Tenth International Conference on Image Processing Theory, 2020
2019
CoRR, 2019
Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019
2018
Mathematical modelling of image processing problems : theoretical studies and applications to joint registration and segmentation. (Modélisation mathématique de problèmes relatifs au traitement d'images : étude théorique et applications aux méthodes conjointes de recalage et de segmentation).
PhD thesis, 2018
A Joint Segmentation/Registration Model Based on a Nonlocal Characterization of Weighted Total Variation and Nonlocal Shape Descriptors.
SIAM J. Imaging Sci., 2018
2017
J. Math. Imaging Vis., 2017
A Unified Hyperelastic Joint Segmentation/Registration Model Based on Weighted Total Variation and Nonlocal Shape Descriptors.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2017
Spatially-Variant Kernel for Optical Flow Under Low Signal-to-Noise Ratios Application to Microscopy.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017
2015