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Automatic Colorization with Improved Spatial Coherence and Boundary Localization

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Abstract

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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Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Wei Zhang & Chao-Wei Fang

  2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Guan-Bin Li

Authors
  1. Wei Zhang
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  2. Chao-Wei Fang
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  3. Guan-Bin Li
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Correspondence to Guan-Bin Li.

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Zhang, W., Fang, CW. & Li, GB. Automatic Colorization with Improved Spatial Coherence and Boundary Localization. J. Comput. Sci. Technol. 32, 494–506 (2017). https://doi.org/10.1007/s11390-017-1739-6

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  • DOI: https://doi.org/10.1007/s11390-017-1739-6

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