Skip to main content
Springer Nature Link
Log in

Colorization for Gray Scale Facial Image by Locality-Constrained Linear Coding

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  1. Markle, W. (1984). The development and application of colorization. SMPTE Journal, 93(7), 632–635.

    Article  Google Scholar 

  2. Welsh, T., Ashikhmin, M., Mueller, K. (2002). Transferring color to greyscale images. ACM Transactions on Graphics, 21(3), 277–280.

    Article  Google Scholar 

  3. Kekre, H.B., & Thepade, S.D. (2008). Color traits transfer to Grayscale Images. In Proceedings of IEEE first international conference on emerging trends in engineering and technology (pp. 82–85).

  4. Levin, A., Lischinski, D., Weiss, Y. (2004). Colorization using optimization. ACM Transactions on Graphics, 23(3), 689–694.

    Article  Google Scholar 

  5. Yatziv, L., & Sapiro, G. (2006). Fast image and video colorization using chrominance blending. IEEE Transactions on Image Processing, 15(5), 1120–1129.

    Article  Google Scholar 

  6. Horiuchi, T., & Hirano, S. (2003). Colorization algorithm for grayscale image by propagating seed pixels. In IEEE international conference on image processing (Vol. 1, pp. 457–460).

  7. Bala, R., & Eschbach, R. (2004). Spatial color-to-grayscale transform preserving chrominance edge information. In 14th color imaging conference: color, science, systems and applications (pp. 82–86).

  8. Gooch, A.A., Olsen, S.C., Tumblin, J., Gooch, B. (2005). Color2gray: salience-preserving color removal. ACM Transactions on Graphics, 24(3), 634–639.

    Article  Google Scholar 

  9. Smith, K., Landes, P.-E., Thollot, J., Myszkowski, K. (2008). Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. In Computer graphics forum (Proc. of EUROGRAPHICS) (Vol. 27, No. 2, pp. 193–200).

  10. Yu, K., Zhang, T., Gong, Y. (2009). Nonlinear learning using local coordinate coding. In Advances in neural information processing systems (Vol. 22, pp. 2223–2231).

  11. Lee, H., Battle, A., Raina, R., Ng, A. (2007). Efficient sparse coding algorithms. In Advances in neural information processing systems (Vol. 19, pp. 801).

  12. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y. (2010). Locality-constrained linear coding for image classification. In IEEE conference on computer vision and pattern recognition (CVPR) (p. 3306).

  13. Efros, A.A., & Leung, T.K. (1999). Texture synthesis by non-parametric sampling. In Proceedings of the seventh IEEE international conference on computer vision (Vol. 2, p. 1033).

  14. Song, M., Chen, C., Bu, J., Sha, T. (2012). Image-based facial sketch-to-photo synthesis via online coupled dictionary learning. Information Sciences.

Download references

Acknowledgments

This work was supported in part by National Natural Science Foundation of China(61170142), National Key Technology R&D Program (2011BAG05B04), International Science & Technology Cooperation Program of China (2013DFG12840), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Yang Liang, Mingli Song, Jiajun Bu & Chun Chen

Authors
  1. Yang Liang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mingli Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiajun Bu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chun Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mingli Song.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liang, Y., Song, M., Bu, J. et al. Colorization for Gray Scale Facial Image by Locality-Constrained Linear Coding. J Sign Process Syst 74, 59–67 (2014). https://doi.org/10.1007/s11265-013-0809-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-013-0809-4

Keywords