›› 2017, Vol. 32 ›› Issue (3): 494-506.doi: 10.1007/s11390-017-1739-6

Special Issue: Computer Graphics and Multimedia

• Special Section of CVM 2017 • Previous Articles     Next Articles

Automatic Colorization with Improved Spatial Coherence and Boundary Localization

Wei Zhang1, Member, IEEE, Chao-Wei Fang1, Guan-Bin Li2,*, Member, IEEE   

  1. 1. Department of Computer Science, The University of Hong Kong, Hong Kong, China;
    2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
  • Received:2016-12-25 Revised:2017-02-26 Online:2017-05-05 Published:2017-05-05
  • Contact: Guan-Bin Li E-mail:liguanbin@mail.sysu.edu.cn
  • About author:Wei Zhang is a Ph.D. candidate at the Department of Computer Science, The University of Hong Kong, Hong Kong. He received his B.E. degree in automation from Chongqing University, Chongqing, in 2010, and M.S. degree in pattern recognition and artificial intelligence from Huazhong University of Science and Technology, Wuhan, in 2013. His research interest covers image colorization and boundary detection.
  • Supported by:

    This work was partially supported by Hong Kong Research Grants Council under General Research Funds (HKU17209714).

Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a pixel-wise prediction task and utilizing deep convolutional neural networks. Though tremendous improvements have been made, the result of automatic colorization is still far from perfect. Specifically, there still exist common pitfalls in maintaining color consistency in homogeneous regions as well as precisely distinguishing colors near region boundaries. To tackle these problems, we propose a novel fully automatic colorization pipeline which involves a boundary-guided CRF (conditional random field) and a CNN-based color transform as post-processing steps. In addition, as there usually exist multiple plausible colorization proposals for a single image, automatic evaluation for different colorization methods remains a challenging task. We further introduce two novel automatic evaluation schemes to efficiently assess colorization quality in terms of spatial coherence and localization. Comprehensive experiments demonstrate great quality improvement in results of our proposed colorization method under multiple evaluation metrics.

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