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Contrast preserving image decolorization combining global features and local semantic features

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Abstract

Image decolorization known as the process to transform a color image to a grayscale one is widely used in single-channel image processing, black and white printing, etc. It is a dimension reduction process which inevitably suffers from information loss. The general goal of image decolorization is to preserve the color contrast of the color image. Traditional image decolorization methods are generally divided into local methods and global methods. However, local methods are not accurate enough to process local pixel blocks which may tend to cause local artifacts. While global methods cannot deal well in local color blocks, which are usually time-consuming, too. Therefore, this paper presents a way to combine the local semantic features and the global features. The traditional image decolorization method uses the low-level features of an image. Instead, in this paper, the convolution neural network is used to learn the global features and local semantic features of an image which can better preserve the contrast in both local color blocks and adjacent pixels of the color image. Finally, the global features and the local semantic features are combined to decolorize the image. Experiments indicate that our method outperforms the state of the arts in terms of contrast preservation.

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References

  1. Hunter, R.S.: Photoelectric color difference meter. J. Opt. Soc. Am. 48(12), 985–993 (1958)

    Article  Google Scholar 

  2. Lu, C.W., Li, X., Jia, J.Y.: Contrast preserving decolorization. In: IEEE International Conference on Computational Photography, pp. 1–7 (2012)

  3. Liu, Q.G., Xiong, J.J., Zhu, L., Zhang, M.H., Wang, Y.H.: Extended RGB2Gray conversion model for efficient contrast preserving decolorization. Multimed. Tools Appl. 76(12), 1–20 (2016)

    Google Scholar 

  4. Sowmya, V., Govind, D., Soman, K.P.: Significance of incorporating chrominance information for effective color-to-grayscale image conversion. Signal Image Video Process. 11(1), 1–8 (2016)

    Google Scholar 

  5. Hou, X.X., Duan, J., Qiu, G.P.: Deep feature consistent deep image transformations: downscaling, decolorization and HDR tone mapping. In: Computer Vision and Pattern Recognition, pp. 1–10 (2017)

  6. Wyszecki, G., Stiles, W.S.: Color science: concepts and methods, quantitative data and formulas. Phys. Today 21(6), 83–84 (1968)

    Article  Google Scholar 

  7. Bala, R., Eschbach, R.: Spatial color-to-grayscale transform preserving chrominance edge information. In: Color and Imaging Conference, pp. 82–86 (2004)

  8. Neumann, L., Cadik, M., Nemcsics, A.: An efficient perception-based adaptive color to gray transformation. In: Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 73–80 (2007)

  9. Smith, K., Landes, P.E., Thollot, J., Myszkowski, K.: Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Comput. Graph. Forum. 27(2), 193–200 (2010)

    Article  Google Scholar 

  10. Zhang, H.C., Liu, S.G.: Efficient decolorization via perceptual group difference enhancement. In: International Conference on Image and Graphics, pp. 560–569 (2017)

  11. Gooch, A.A., Olsen, S.C., Tumblin, J., Gooch, B.: Color2Gray: salience-preserving color removal. In: ACM SIGGRAPH, pp. 634–639 (2005)

  12. Kuk, J.G., Ahn, J.H., Cho, N.I.: A color to grayscale conversion considering local and global contrast. In: Asian Conference on Computer Vision, pp. 513–524 (2010)

  13. Grundland, M., Dodgson, N.A.: Decolorize: fast, contrast enhancing, color to grayscale conversion. Pattern Recognit. 40(11), 2891–2896 (2007)

    Article  Google Scholar 

  14. Liu, Q.G., Xiong, J.J., Zhu, L., Zhang, M.H., Wang, Y.H.: GcsDecolor: gradient correlation similarity for efficient contrast preserving decolorization. IEEE Trans. Image Process. 24(9), 2889–2904 (2015)

    Article  MathSciNet  Google Scholar 

  15. Xue, W.F., Zhang, L., Mou, X.Q., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kim, Y.J., Jang, C., Demouth, J., Lee, S.: Robust color-to-gray via nonlinear global mapping. In: ACM SIGGRAPH Asia. Article 161 (2009)

  17. Song, M.L., Tao, D.C., Chen, C., Li, X.L., Chen, C.W.: Color to gray: visual cue preservation. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1537–1552 (2010)

    Article  Google Scholar 

  18. Ancuti, C.O., Ancuti, C., Bekaert, P.: Enhancing by saliency-guided decolorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 257–264 (2011)

  19. Lu, C.W., Xu, L., Jia, J.Y.: Real-time contrast preserving decolorization. In: SIGGRAPH Asia Technical Briefs, pp. 1–4 (2012)

  20. Lu, C.W., Xu, L., Jia, J.Y.: Contrast preserving decolorization with perception-based quality metrics. Int. J. Comput. Vis. 110(2), 222–239 (2014)

    Article  Google Scholar 

  21. Song, Y.B., Bao, L.C., Xu, X.B., Yang, Q.X.: Decolorization: is rgb2gray() out?. In: SIGGRAPH Asia Technical Briefs, pp. 1–4 (2013)

  22. Ji, Z.P., Fang, M.E., Wang, Y.G., Ma, W.T.: Efficient decolorization preserving dominant distinctions. Vis. Comput. 32(12), 1–11 (2016)

    Article  Google Scholar 

  23. Lin, Z.C., Zhang, W., Tang, X.O.: Learning partial differential equations for computer vision. Microsoft technical report. MSR-TR-2008-189 (2008)

  24. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

  25. Cadik, M.: Perceptual evaluation of color-to-grayscale image conversions. Comput. Graph. Forum 27(7), 1745–1754 (2008)

    Article  Google Scholar 

  26. Wu, T., Toet, A.: Color-to-grayscale conversion through weighted multiresolution channel fusion. J. Electron. Imaging 23(4), 1–6 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Software, Tianjin University, Tianjin, China

    Xiaoli Zhang

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Shiguang Liu

Authors
  1. Xiaoli Zhang
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  2. Shiguang Liu
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Correspondence to Shiguang Liu.

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Funding

This study was funded by the Natural Science Foundation of China (Grant Numbers 61672375 and 61170118).

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The authors declare that they have no conflict of interest.

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Zhang, X., Liu, S. Contrast preserving image decolorization combining global features and local semantic features. Vis Comput 34, 1099–1108 (2018). https://doi.org/10.1007/s00371-018-1524-8

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