Mapping-based image diffusion
In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application-dependent and contextual information using first principles. Few works in literature treat variational models that describe b...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| In: |
Journal of mathematical imaging and vision
Year: 2016, Jahrgang: 57, Heft: 3, Pages: 293-323 |
| ISSN: | 1573-7683 |
| DOI: | 10.1007/s10851-016-0672-6 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/s10851-016-0672-6 Verlag, Volltext: https://link.springer.com/article/10.1007/s10851-016-0672-6 
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| Verfasserangaben: | Freddie Åström, Michael Felsberg, George Baravdish |
MARC
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| 520 | |a In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application-dependent and contextual information using first principles. Few works in literature treat variational models that describe both application-dependent information and contextual knowledge of the denoising problem. We prove the existence of a minimizer and present results on tensor symmetry constraints, convexity, and geometric interpretation of the proposed functional. We show that our framework excels in applications where nonlinear functions are present such as in gamma correction and targeted value range filtering. We also study general denoising performance where we show comparable results to dedicated PDE-based state-of-the-art methods. | ||
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