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

De Castro, Yohann; Duval, Vincent; Petit, Romain (2022), Towards Off-the-Grid Algorithms for Total Variation Regularized Inverse Problems, Journal of Mathematical Imaging and Vision, p. 25. 10.1007/s10851-022-01115-w

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Type
Article accepté pour publication ou publié
Date
2022
Journal name
Journal of Mathematical Imaging and Vision
Publisher
Springer
Pages
25
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
Total variation; Inverse problems; Off-the-grid imaging