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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/MCG.2019.2899089
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhang, Wei
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Automatic Color Sketch Generation Using Deep Style Transfer
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| 264 |
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|c 2019
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|a Text
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|a Date Revised 20.11.2019
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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 Recent advances in deep learning based algorithms have made it feasible to transfer image styles from an example image to other images. However, it is still hard to transfer the style of color sketches due to their unique texture statistics. In this paper, an automatic color sketch generation system is developed from existing real-time style transfer methods. We choose a suitable image from a set of carefully selected color sketch examples as the style target for every content image during training. We also propose a novel style transfer convolutional neural network with spatial refinement to realize high-resolution style transfer. Finally, gouache color is introduced to the generated images via a linear color transform followed by a guided filtering operation. Experimental results illustrate that our system can produce vivid color sketch images and greatly reduce artifacts compared to previous state-of-the-art methods
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Li, Guanbin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ma, Haoyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Yizhou
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE computer graphics and applications
|d 1991
|g 39(2019), 2 vom: 11. März, Seite 26-37
|w (DE-627)NLM098172794
|x 1558-1756
|7 nnas
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| 773 |
1 |
8 |
|g volume:39
|g year:2019
|g number:2
|g day:11
|g month:03
|g pages:26-37
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| 856 |
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|u http://dx.doi.org/10.1109/MCG.2019.2899089
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