Regularization with sparse vector fields: From image compression to TV-type reconstruction
Brinkmann EM, Burger M, Grah J (2015)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Edited Volumes: Scale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings
Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages Range: 191-202
Event location: Bordeaux
ISBN: 9783319184609
DOI: 10.1007/978-3-319-18461-6_16
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.
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Germany (DE)How to cite
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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