EmNeF: Neural Fields for Embedded Variational Problems in Imaging

Danielle Bednarski; Jan Lellmann

doi:10.1007/978-3-031-31975-4_11

EmNeF: Neural Fields for Embedded Variational Problems in Imaging

Danielle Bednarski^*, Jan Lellmann

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Mathematische Methoden der Bildverarbeitung

Abstract

We propose a model-driven neural fields approach for solving variational problems. The approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem.

Originalsprache	Englisch
Titel	Scale Space and Variational Methods in Computer Vision : 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings
Seitenumfang	12
Herausgeber (Verlag)	Springer, Cham
Erscheinungsdatum	10.05.2023
Seiten	137 - 148
ISBN (Print)	9783031319747
DOIs	https://doi.org/10.1007/978-3-031-31975-4_11
Publikationsstatus	Veröffentlicht - 10.05.2023

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Dokumente

10.1007/978-3-031-31975-4_11

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Bednarski, D., & Lellmann, J. (2023). EmNeF: Neural Fields for Embedded Variational Problems in Imaging. in Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings (S. 137 - 148). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14009 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_11

Bednarski, Danielle ; Lellmann, Jan. / EmNeF: Neural Fields for Embedded Variational Problems in Imaging. Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings. Springer, Cham, 2023. S. 137 - 148 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We propose a model-driven neural fields approach for solving variational problems. The approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem.",

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doi = "10.1007/978-3-031-31975-4\_11",

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Bednarski, D & Lellmann, J 2023, EmNeF: Neural Fields for Embedded Variational Problems in Imaging. in Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 14009 LNCS, Springer, Cham, S. 137 - 148. https://doi.org/10.1007/978-3-031-31975-4_11

EmNeF: Neural Fields for Embedded Variational Problems in Imaging. / Bednarski, Danielle; Lellmann, Jan.
Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings. Springer, Cham, 2023. S. 137 - 148 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14009 LNCS).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

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PY - 2023/5/10

Y1 - 2023/5/10

N2 - We propose a model-driven neural fields approach for solving variational problems. The approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem.

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Bednarski D, Lellmann J. EmNeF: Neural Fields for Embedded Variational Problems in Imaging. in Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023, Proceedings. Springer, Cham. 2023. S. 137 - 148. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-31975-4_11