TY - GEN
T1 - EmNeF: Neural Fields for Embedded Variational Problems in Imaging
AU - Bednarski, Danielle
AU - Lellmann, Jan
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
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.
AB - 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.
UR - https://rdcu.be/dhGx9
UR - https://www.mendeley.com/catalogue/e0fd50ce-8c3d-3050-9de7-1f8bac969cce/
U2 - 10.1007/978-3-031-31975-4_11
DO - 10.1007/978-3-031-31975-4_11
M3 - Conference contribution
SN - 9783031319747
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 148
BT - Scale Space and Variational Methods in Computer Vision
PB - Springer, Cham
ER -
