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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3326682
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Ning
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Region Assisted Sketch Colorization
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| 264 |
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|c 2023
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|a Text
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|a Date Revised 09.11.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Automatic sketch colorization is a challenging task that aims to generate a color image from a sketch, primarily due to its inherently ill-posed nature. While many approaches have shown promising results, two significant challenges remain: limited color patterns and a wide range of artifacts such as color bleeding and semantic inconsistencies among relevant regions. These issues stem from the operation of traditional convolutional structures, which capture structural features in a pixel-wise manner, resulting in inadequate utilization of regional information within the sketch. Therefore, we propose the Region-Assisted Sketch Coloring (RASC) method, which introduces an intermediate representation called the 'Region Map' to explicitly characterize the regional information of the sketch. This Region Map is derived from the input sketch and is effectively formulated by our RASC architecture, enhancing the perception of region-wise features beyond the original pixel-wise features. Specifically, we start by employing the sketch encoder to extract hierarchical feature maps from the input sketches. Subsequently, we introduce a coarse-to-fine decoder comprising a series of Region-based Modulation (RM) blocks. This decoder modulates features that combine the modulation results of its previous block and the sketch features of the corresponding encoder block with our Region Formulation module. Each module explicitly formulates the sketch features in a region-wise manner. This accurately captures both the inner-region local style and inter-region global context dependency, resulting in various color patterns and fewer synthesis artifacts. Our experimental results show that our proposed method surpasses state-of-the-art methods in both synthetic and real sketch datasets
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|a Journal Article
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| 700 |
1 |
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|a Niu, Muyao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zhihui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Kun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zhiyong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Haojie
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 22., Seite 6142-6154
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
1 |
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|g volume:32
|g year:2023
|g day:22
|g pages:6142-6154
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2023.3326682
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