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- Publisher Website: 10.1109/MCG.2019.2899089
- Scopus: eid_2-s2.0-85061530669
- PMID: 30762535
- WOS: WOS:000462397900005
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Article: Automatic Color Sketch Generation Using Deep Style Transfer
Title | Automatic Color Sketch Generation Using Deep Style Transfer |
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Authors | |
Keywords | Image color analysis Transforms Training Computer architecture Real-time systems |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at http://www.computer.org/cga |
Citation | IEEE Computer Graphics and Applications, 2019, v. 39 n. 2, p. 26-37 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/271348 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.385 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | ZHANG, W | - |
dc.contributor.author | LI, G | - |
dc.contributor.author | MA, H | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2019-06-24T01:08:08Z | - |
dc.date.available | 2019-06-24T01:08:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Computer Graphics and Applications, 2019, v. 39 n. 2, p. 26-37 | - |
dc.identifier.issn | 0272-1716 | - |
dc.identifier.uri | http://hdl.handle.net/10722/271348 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://www.computer.org/cga | - |
dc.relation.ispartof | IEEE Computer Graphics and Applications | - |
dc.rights | IEEE Computer Graphics and Applications. Copyright © IEEE. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Image color analysis | - |
dc.subject | Transforms | - |
dc.subject | Training | - |
dc.subject | Computer architecture | - |
dc.subject | Real-time systems | - |
dc.title | Automatic Color Sketch Generation Using Deep Style Transfer | - |
dc.type | Article | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/MCG.2019.2899089 | - |
dc.identifier.pmid | 30762535 | - |
dc.identifier.scopus | eid_2-s2.0-85061530669 | - |
dc.identifier.hkuros | 297942 | - |
dc.identifier.volume | 39 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 26 | - |
dc.identifier.epage | 37 | - |
dc.identifier.isi | WOS:000462397900005 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0272-1716 | - |