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Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems

De Castro, Yohann; Duval, Vincent; Petit, Romain (2021), Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems, in Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon, Scale Space and Variational Methods in Computer Vision, Proceedings of SSVM 2021, p. 553-564. 10.1007/978-3-030-75549-2_44

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Type
Communication / Conférence
Date
2021
Conference title
8th International Conference on Scale Space and Variational Methods in Computer Vision
Conference date
2021-05
Conference city
Online
Book title
Scale Space and Variational Methods in Computer Vision
Book author
Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon
Publisher
Proceedings of SSVM 2021
ISBN
978-3-030-75548-5;978-3-030-75549-2
Number of pages
580
Pages
553-564
Publication identifier
Metadata
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Author(s)
De Castro, Yohann
Institut Camille Jordan [ICJ]
Duval, Vincent cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Petit, Romain
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most existing methods, that produce an approximate solution which is piecewise constant on a fixed mesh, our approach exploits the structure of the solutions and consists in iteratively constructing a linear combination of indicator functions of simple polygons.
Subjects / Keywords
Off-the-grid imaging; Inverse problems; Total variation