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|a 10.1109/TVCG.2019.2908363
|2 doi
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|a eng
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| 100 |
1 |
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|a Fang, Faming
|e verfasserin
|4 aut
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| 245 |
1 |
2 |
|a A Superpixel-Based Variational Model for Image Colorization
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 02.09.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Image colorization refers to a computer-assisted process that adds colors to grayscale images. It is a challenging task since there is usually no one-to-one correspondence between color and local texture. In this paper, we tackle this issue by exploiting weighted nonlocal self-similarity and local consistency constraints at the resolution of superpixels. Given a grayscale target image, we first select a color source image containing similar segments to target image and extract multi-level features of each superpixel in both images after superpixel segmentation. Then a set of color candidates for each target superpixel is selected by adopting a top-down feature matching scheme with confidence assignment. Finally, we propose a variational approach to determine the most appropriate color for each target superpixel from color candidates. Experiments demonstrate the effectiveness of the proposed method and show its superiority to other state-of-the-art methods. Furthermore, our method can be easily extended to color transfer between two color images
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Wang, Tingting
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zeng, Tieyong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Guixu
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 26(2020), 10 vom: 01. Okt., Seite 2931-2943
|w (DE-627)NLM098269445
|x 1941-0506
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|g volume:26
|g year:2020
|g number:10
|g day:01
|g month:10
|g pages:2931-2943
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|u http://dx.doi.org/10.1109/TVCG.2019.2908363
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