Regularization with sparse vector fields: From image compression to TV-type reconstruction

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Details zur Publikation

Autorinnen und Autoren: Brinkmann EM, Burger M, Grah J
Titel Sammelwerk: Scale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings
Verlag: Springer Verlag
Jahr der Veröffentlichung: 2015
Titel der Reihe: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Seitenbereich: 191-202
ISBN: 9783319184609
ISSN: 0302-9743
Sprache: Englisch


Abstract

This paper introduces a novel variational approach for image compression motivated by recent PDE-based approaches combining edge detection and Laplacian inpainting. The essential feature is to encode the image via a sparse vector field, ideally concentrating on a set of measure zero. An equivalent reformulation of the compression approach leads to a variational model resembling the ROF-model for image denoising, hence we further study the properties of the effective regularization functional introduced by the novel approach and discuss similarities to TV and TGV functionals. Moreover, we computationally investigate the behaviour of the model with sparse vector fields for compression in particular for high resolution images and give an outlook towards denoising.


Einrichtungen weiterer Autorinnen und Autoren

Westfälische Wilhelms-Universität (WWU) Münster


Zitierweisen

APA:
Brinkmann, E.M., Burger, M., & Grah, J. (2015). Regularization with sparse vector fields: From image compression to TV-type reconstruction. In Proceedings of the 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015 (pp. 191-202). Bordeaux: Springer Verlag.

MLA:
Brinkmann, Eva Maria, Martin Burger, and Joana Grah. "Regularization with sparse vector fields: From image compression to TV-type reconstruction." Proceedings of the 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, Bordeaux Springer Verlag, 2015. 191-202.

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Zuletzt aktualisiert 2019-21-08 um 21:53