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2016 | OriginalPaper | Buchkapitel

17. Neighborhood Similarity-Based Color Transfer Algorithm

verfasst von : Yanhao Li, Zhijiang Li, Liqin Cao

Erschienen in: Advanced Graphic Communications, Packaging Technology and Materials

Verlag: Springer Singapore

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Abstract

Color transfer produces a new image and keeps the content of source image and the color of reference image. The technology is widely used in areas such as prepress image processing and multimedia post-production. The article proposes an adaptive local color transfer algorithm based on neighborhood similarity. Firstly, two classical algorithms—Reinhard algorithm and Welsh algorithm—are analyzed on the applicability and deficiency according to the experiments. Then, a new method is introduced to improve Welsh algorithm on incomplete point neighborhood description and the low efficiency of searching for matching points, which is calculating the neighborhood average value by adding high-frequency texture information to describe neighborhood features, and local hierarchical matching points searching strategy. The matching points searching strategy includes dividing the target image into blocks, searching for matching point based on neighborhood similarity firstly, and searching the global image to find best matching points finally. Experiments demonstrate that the improved algorithm can transfer the referential color effectively without any user intervention.

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Literatur
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Zurück zum Zitat Pan, J., Wang, X. S., & Cheng, Y. H. (2014). Multi-source color transfer based on active contours exploration. Acta Automatica Sinica, 40(4), 713–720. Pan, J., Wang, X. S., & Cheng, Y. H. (2014). Multi-source color transfer based on active contours exploration. Acta Automatica Sinica, 40(4), 713–720.
2.
Zurück zum Zitat Reinhard, E., Ashikhmin, M., Gooch, B., & Shirley, P. (2001). Color transfer between images. Computer Graphics & Applications IEEE, 21(5), 34–41.CrossRef Reinhard, E., Ashikhmin, M., Gooch, B., & Shirley, P. (2001). Color transfer between images. Computer Graphics & Applications IEEE, 21(5), 34–41.CrossRef
3.
Zurück zum Zitat Welsh, T., Ashikhmin, M., & Mueller, K. (2002). Transferring color to greyscale images. Siggraph Proceedings of Annual Conference on Computer Graphics & Interactive Technique, 21(3), 277–280. Welsh, T., Ashikhmin, M., & Mueller, K. (2002). Transferring color to greyscale images. Siggraph Proceedings of Annual Conference on Computer Graphics & Interactive Technique, 21(3), 277–280.
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Zurück zum Zitat Ruderman, D. L. (1998). Statistics of cone responses to natural images: Implications for visual coding. Journal of Optical Society America, 15(8), 2036–2045.CrossRef Ruderman, D. L. (1998). Statistics of cone responses to natural images: Implications for visual coding. Journal of Optical Society America, 15(8), 2036–2045.CrossRef
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Zurück zum Zitat Wang, M., & Chen, Z. (2011). A color transfer algorithm based on neighborhood correlation and optimization techniques. In Computational intelligence and design, international symposium on (Vol. 2, pp. 31–34). New York: IEEE. Wang, M., & Chen, Z. (2011). A color transfer algorithm based on neighborhood correlation and optimization techniques. In Computational intelligence and design, international symposium on (Vol. 2, pp. 31–34). New York: IEEE.
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Zurück zum Zitat Levin, A., Lischinski, D., & Weiss, Y. (2004). Colorization using optimization. ACM Transactions on Graphics, 23(3), 686–691.CrossRef Levin, A., Lischinski, D., & Weiss, Y. (2004). Colorization using optimization. ACM Transactions on Graphics, 23(3), 686–691.CrossRef
Metadaten
Titel
Neighborhood Similarity-Based Color Transfer Algorithm
verfasst von
Yanhao Li
Zhijiang Li
Liqin Cao
Copyright-Jahr
2016
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-0072-0_17

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