Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866915370739171328 |
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| author | de Castro, Yohann Duval, Vincent Petit, Romain |
| author_facet | de Castro, Yohann Duval, Vincent Petit, Romain |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_06706 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems de Castro, Yohann Duval, Vincent Petit, Romain Signal Processing Numerical Analysis Optimization and Control 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. |
| title | Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems |
| topic | Signal Processing Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2104.06706 |