Iterative TV minimization on the graph
We define the space of functions of bounded variation (BV) on the graph. Using the notion of divergence of flows on graphs, we show that the unit ball of the dual space to BV in the graph setting can be described as the image of the unit ball of the space l(infinity) by the divergence operator. Base...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| In: |
Communications in mathematical sciences
Year: 2019, Jahrgang: 17, Heft: 4, Pages: 941-968 |
| ISSN: | 1539-6746 |
| DOI: | 10.4310/CMS.2019.v17.n4.a4 |
| Online-Zugang: | Verlag: https://dx.doi.org/10.4310/CMS.2019.v17.n4.a4
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| Verfasserangaben: | Japhet Niyobuhungiro, Eric Setterqvist, Freddie Astroem, George Baravdish |
MARC
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| 520 | |a We define the space of functions of bounded variation (BV) on the graph. Using the notion of divergence of flows on graphs, we show that the unit ball of the dual space to BV in the graph setting can be described as the image of the unit ball of the space l(infinity) by the divergence operator. Based on this result, we propose a new iterative algorithm to find the exact minimizer for the total variation (TV) denoising problem on the graph. The proposed algorithm is provable convergent and its performance on image denoising examples is compared with the Split Bregman and Primal-Dual algorithms as benchmarks for iterative methods and with BM3D as a benchmark for other state-of-the-art denoising methods. The experimental results show highly competitive empirical convergence rate and visual quality for the proposed algorithm. | ||
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| 650 | 4 | |a constrained total variation | |
| 650 | 4 | |a image decomposition | |
| 650 | 4 | |a Primal-dual | |
| 650 | 4 | |a ROF model on the graph | |
| 650 | 4 | |a Split Bregman | |
| 650 | 4 | |a texture | |
| 650 | 4 | |a Total variation | |
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