Skip to main content
Log in

Fast color transfer from multiple images

  • Published:
Applied Mathematics-A Journal of Chinese Universities Aims and scope Submit manuscript

Abstract

Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the correspondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A Abadpour, S Kasaei. A fast and efficient fuzzy color transfer method, In: Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004, 491–494.

    Google Scholar 

  2. X An, F Pellacini. User-controllable color transfer, Comput Graph Forum, 2010, 29(2): 263–271.

    Article  Google Scholar 

  3. X An, F Pellacini. AppProp: all-pairs appearance-space edit propagation, ACM Trans Graph, 2008, 27(3), no 40.

    Google Scholar 

  4. Y Chang, S Saito, M Nakajima. Example-based color transformation of image and video using basic color categories, IEEE Trans Image Process, 2007, 16(2): 329–336.

    Article  MathSciNet  Google Scholar 

  5. Y Chang, S Saito, K Uchikawa, M Nakajima. Example-based color stylization of images, ACM Trans Appl Percept, 2005, 2(3): 322–345.

    Article  Google Scholar 

  6. X Chen, D Zou, Q Zhao, P Tan. Manifold preserving edit propagation, ACM Trans Graph, 2012, 31(6), no 132.

    Google Scholar 

  7. A Y S Chia, S Zhuo, R K Gupta, Y W Tai, S Y Cho, P Tan, S Lin. Semantic colorization with internet images, ACM Trans Graph, 2011, 30(6), no 156.

    Google Scholar 

  8. O D Cohen, O Sorkine, R Gal, T Leyvand, Y Q Xu. Color harmonization, ACM Trans Graph, 2006, 25(3): 624–630.

    Article  Google Scholar 

  9. A Criminisi, T Sharp, C Rother, P Perez. Geodesic image and video editing, ACM Trans Graph, 2010, 29(5), no 134.

    Google Scholar 

  10. Z Farbman, R Fattal, D Lischinski. Diffusion maps for edge-aware image editing, ACM Trans Graph, 2010, 29(6), no 145.

    Google Scholar 

  11. H G Ha. Local color transfer using modified color influence map with color category, In: IEEE International Conference on Consumer Electronics-Berlin, 2011, 194–197.

    Google Scholar 

  12. J H Kim, D K Shin, Y S Moon. Color transfer in images based on separation of chromatic and achromatic colors, In: International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications, 2009, 285–296.

    Google Scholar 

  13. Y Li, E H Adelson, A Agarwala. Scribbleboost: Adding classification to edge-aware interpolation of local image and video adjustments, Comput Graph Forum, 2008, 27(4): 1255–1264.

    Article  Google Scholar 

  14. D Lischinski, Z Farbman, M Uyttendaele, R Szeliski. Interactive local adjustment of tonal values, ACM Trans Graph, 2006, 25(3): 646–653.

    Article  Google Scholar 

  15. X Liu, L Wan, Y Qu, T T Wong, S Lin, C S Leung, P A Heng. Intrinsic colorization, ACM Trans Graph, 2008, 27(5), no 152.

    Google Scholar 

  16. A Maslennikova, V Vezhnevets. Interactive local color transfer between images, In: GraphiCon’ 2007, 2007.

    Google Scholar 

  17. P Musialski, M Cui, J Ye, A Razdan, P Wonka. A framework for interactive image color editing, Vis Comput, 2013, 29: 1173–1186.

    Article  Google Scholar 

  18. L Neumann, A Neumann. Color style transfer techniques using hue lightness and saturation histogram matching, In: Computational Aesthetics in Graphics, Visualization and Imaging, The Eurographics Association, 2005, 111–122.

    Google Scholar 

  19. F Pitie, A C Kokaram, R Dahyot. N-dimensional probability density function transfer and its application to color transfer, In: Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, 2: 1434–1439

    Article  Google Scholar 

  20. F Pitie, A C Kokaram, R Dahyot. Automated colour grading using colour distribution transfer, Comput Vis Image Underst, 2007, 107(1-2): 123–137.

    Article  Google Scholar 

  21. F Pitie, A Kokaram. The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer, In: IET 4th European Conference on Visual Media Production, 2007, 1–9.

    Google Scholar 

  22. T Pouli, E Reinhard. Progressive color transfer for images of arbitrary dynamic range, Comput Graph, 2011, 35: 67–80.

    Article  Google Scholar 

  23. E Reinhard, M Ashikhmin, B Gooch, P Shirley. Color transfer between images, IEEE Comput Graph Appl, 2001, 21(5): 34–41.

    Article  Google Scholar 

  24. S T Roweis, L K Saul. Nonlinear dimensionality reduction by locally linear embedding, Science, 2000, 290(5500): 2323–2326.

    Article  Google Scholar 

  25. L K Saul, S T Roweis. Think globally, fit locally: unsupervised learning of low dimensional manifolds, J Mach Learn Res, 2003, 4(2): 119–155.

    MathSciNet  MATH  Google Scholar 

  26. L Shapira, A Shamir, O D Cohen. Image appearance exploration by model-based navigation, Comput Graph Forum, 2009, 28(2): 629–638.

    Article  Google Scholar 

  27. V De Silva, J B Tenenbaum. Sparse multidimensional scaling using landmark points, Technical report, Stanford University, 2004.

    Google Scholar 

  28. Y W Tai, J Jia, C K Tang. Local color transfer via probabilistic segmentation by expectationmaximization, In: IEEE Conference on Computer Vision and Pattern Recognition, 2005, 1: 747–754.

    Google Scholar 

  29. Y W Tai, J Jia, C K Tang. Soft color segmentation and its applications, IEEE Trans Pattern Anal Mach Intell, 2007, 29(9): 1520–1537.

    Article  Google Scholar 

  30. B Wang, Y Yu, T T Wong, C Chen, Y Q Xu. Data-driven image color theme enhancement, ACM Trans Graph, 2010, 29(6), no 146.

    Google Scholar 

  31. B Wang, Y Yu, Y Q Xu. Example-based image color and tone style enhancement, ACM Trans Graph, 2011, 30(4), no 64.

    Google Scholar 

  32. T Welsh, M Ashikhmin, K Mueller. Transferring color to grayscale images, ACM Trans Graph, 2002, 21(3): 277–280.

    Article  Google Scholar 

  33. X Xiao, L Ma. Color transfer in correlated color space, In: Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and its Applications, 2006, 305–309.

    Google Scholar 

  34. X Xiao, L Ma. Gradient-preserving color transfer, Comput Graph Forum, 2009, 28(7): 1879–1886.

    Article  Google Scholar 

  35. C-K Yang, L-K Peng. Automatic mood-transferring between color images, IEEE Comput Graph Appl, 2008, 28(2): 52–61.

    Article  MathSciNet  Google Scholar 

  36. J-D Yoo, M K Park, K H Lee. Local color transfer between images using dominant colors, J Electron Imaging, 2013, 22(3), 033003.

    Google Scholar 

Download references

Acknowledgments

We would like to thanks Sakandar Hayat for proofreading the manuscript and anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Asad Khan.

Additional information

This work is supported by the National Natural Science Foundation of China (61672482, 11626253) and the One Hundred Talent Project of the Chinese Academy of Sciences.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., Jiang, L., Li, W. et al. Fast color transfer from multiple images. Appl. Math. J. Chin. Univ. 32, 183–200 (2017). https://doi.org/10.1007/s11766-017-3447-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11766-017-3447-y

MR Subject Classification

Keywords

Navigation