@inproceedings{bf2c85553d9146f2889098d34d5ce4e4,
title = "Learned Discretization Schemes for the Second-Order Total Generalized Variation",
abstract = "The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the total variation, there has been recent work to improve the second-order total generalized variation discretization, based on the same design idea. In this work, we propose to extend this to a general discretization scheme based on interpolation filters, for which we prove variational consistency. We then describe how to learn these interpolation filters to optimize the discretization for various imaging applications. We illustrate the performance of the method on a synthetic data set as well as for natural image denoising.",
keywords = "bilevel optimization, discretization, image denoising, learning, piggyback algorithm, primal-dual algorithms, Total generalized variation",
author = "Lea Bogensperger and Antonin Chambolle and Alexander Effland and Thomas Pock",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 9th International Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2023, SSVM 2023 ; Conference date: 21-05-2023 Through 25-05-2023",
year = "2023",
doi = "10.1007/978-3-031-31975-4_37",
language = "English",
isbn = "9783031319747",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "484--497",
editor = "Luca Calatroni and Marco Donatelli and Serena Morigi and Marco Prato and Matteo Santacesaria",
booktitle = "Scale Space and Variational Methods in Computer Vision - 9th International Conference, SSVM 2023, Proceedings",
address = "Germany",
}