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Volume 30 Issue 3
May  2015
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Han-Li Zhao, Gui-Zhi Nie, Xu-Jie Li, Xiao-Gang Jin, Zhi-Geng Pan. Structure-Aware Nonlocal Optimization Framework for Image Colorization[J]. Journal of Computer Science and Technology, 2015, 30(3): 478-488. DOI: 10.1007/s11390-015-1538-x
Citation: Han-Li Zhao, Gui-Zhi Nie, Xu-Jie Li, Xiao-Gang Jin, Zhi-Geng Pan. Structure-Aware Nonlocal Optimization Framework for Image Colorization[J]. Journal of Computer Science and Technology, 2015, 30(3): 478-488. DOI: 10.1007/s11390-015-1538-x
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Structure-Aware Nonlocal Optimization Framework for Image Colorization

  • 1. Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China;
    2. State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China 3Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 311121, China

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 61100146 and 61472351, and the Zhejiang Provincial Natural Science Foundation of China under Grant Nos. LY15F020019 and LQ14F020006. Pan was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2013BAH24F01.
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  • Author Bio:

    Han-Li Zhao is an associate professor at Wenzhou University, Wenzhou. He received his B.E. degree in software engineering from Sichuan University in 2004 and Ph.D. degree in computer science from Zhejiang University in 2009. He is a member of CCF and ACM. His current research interests include interactive image editing, computer animation, geometric processing, and GPU parallel computing.

  • Received Date: September 07, 2014
  • Revised Date: March 15, 2015
  • Published Date: May 04, 2015
    Abstract
  • This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colorization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing K nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.
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    • References (26)
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