http://www.cnr.it/ontology/cnr/individuo/prodotto/ID8130
Variational models for image colorization via Chromaticity and Brightness decomposition (Articolo in rivista)
- Type
- Label
- Variational models for image colorization via Chromaticity and Brightness decomposition (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TIP.2007.903257 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Kang S.H., March R. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Scopus (literal)
- ISI Web of Science (WOS) (literal)
- athematical Reviews on the web (MathSciNet) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- University of Kentucky (USA); IAC-CNR (literal)
- Titolo
- Variational models for image colorization via Chromaticity and Brightness decomposition (literal)
- Abstract
- Colorization refers to an image processing task which recovers color in grayscale images when only small regions with color are given. We propose a couple of variational models using chromaticity color components to colorize black and white images. We first consider total variation minimizing (TV) colorization which is an extension from TV inpainting to color using chromaticity model. Second, we further modify our model to weighted harmonic maps for colorization. This model adds edge information from the brightness data, while it reconstructs smooth color values for each homogeneous region. We introduce penalized versions of the variational models, we analyze their convergence properties, and we present numerical results including extension to texture colorization. (literal)
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- Autore CNR
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