Non-local Recoloring Algorithm for Color Vision Deficiencies with Naturalness and Detail Preserving

  • Yunlu Wang
  • Duo Li
  • Menghan HuEmail author
  • Liming CaiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)


People with Color Vision Deficiencies (CVD) may have difficulty in recognizing and communicating color information, especially in the multimedia era. In this paper, we proposed a recoloring algorithm to enhance visual perception of people with CVD. In the algorithm, color modification for color blindness is conducted in HSV color space under three constraints: detail, naturalness and authenticity. A new non-local recoloring method is used for preserving details. Subjective experiments were conducted among normal vision subjects and color blind subjects. Experimental results show that our algorithm is robust, detail preserving and maintains naturalness. (Source codes are freely available to non-commercial users at the website (


Color blind Recoloring Color vision deficiency Non-local algorithm 



This work is sponsored by the Shanghai Sailing Program (No. 19YF1414100), the National Natural Science Foundation of China (No. 61831015, No. 61901172), the STCSM (No. 18DZ2270700), and the China Postdoctoral Science Foundation funded project (No. 2016M600315).


  1. 1.
    Young, T.: II. The Bakerian lecture. On the theory of light and colours. Philos. Trans. R. Soc. Lond. 92, 12–48 (1802)Google Scholar
  2. 2.
    Svaetichin, G.: Spectral response curves from single cones. Acta Physiol. Scand. Suppl. 39(134), 17–46 (1956)Google Scholar
  3. 3.
    Cisco Systems, Inc.: Cisco visual networking index: forecast and trends (2017–2022). Accessed 27 Feb 2019
  4. 4.
    Huang, et al.: Enhancing color representation for the color vision impaired. In: Workshop on Computer Vision Applications for the Visually Impaired (2008)Google Scholar
  5. 5.
    Brettel, H., et al.: Computerized simulation of color appearance for dichromats. JOSA A 14(10), 2647–2655 (1997)CrossRefGoogle Scholar
  6. 6.
    Yaguchi, H., et al.: Computerized simulation of color appearance for anomalous trichromats using the multispectral image. JOSA A 35(4), B278–B286 (2018)CrossRefGoogle Scholar
  7. 7.
    Pendhari, N., et al.: Color modification system for barrier free vision. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–4 (2017)Google Scholar
  8. 8.
    Jenny, B., et al.: Color design for the color vision impaired. Cartogr. Perspect. 58, 61–67 (2007)CrossRefGoogle Scholar
  9. 9.
    Bischof, et al.: BLINDSCHEMES: Stata module to provide graph schemes sensitive to color vision deficiency (2019).
  10. 10.
    Huang, J.B., et al.: Image recolorization for the colorblind. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1161–1164 (2009)Google Scholar
  11. 11.
    Rasche, K., et al.: Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput. Graph. Appl. 25(3), 22–30 (2005)CrossRefGoogle Scholar
  12. 12.
    Doliotis, P., et al.: Intelligent modification of colors in digitized paintings for enhancing the visual perception of color-blind viewers. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 293–301 (2009)Google Scholar
  13. 13.
    Ruminski, J., et al.: Color transformation methods for dichromats. In: 3rd International Conference on Human System Interaction, pp. 634–641 (2010)Google Scholar
  14. 14.
    Huang, J.B., et al.: Information preserving color transformation for protanopia and deuteranopia. IEEE Signal Process. Lett. 14(10), 711–714 (2007)CrossRefGoogle Scholar
  15. 15.
    Hassan, M.F., et al.: Naturalness preserving image recoloring method for people with red–green deficiency. Sig. Process. Image Commun. 57, 126–133 (2017)CrossRefGoogle Scholar
  16. 16.
    Xu, Q., Zhang, X., Zhang, L., Zhu, G., Song, J., Shen, P.: An efficient recoloring method for color vision deficiency based on color confidence and difference. In: Yang, J., et al. (eds.) CCCV 2017. CCIS, vol. 771, pp. 270–281. Springer, Singapore (2017). Scholar
  17. 17.
    Zhu, Z., et al.: Naturalness-and information-preserving image recoloring for red–green dichromats. Sig. Process. Image Commun. 76, 68–80 (2019)CrossRefGoogle Scholar
  18. 18.
    Doron, R., et al.: Spatial visual function in anomalous trichromats: Is less more? PLoS ONE 14(1), e0209662 (2019)CrossRefGoogle Scholar
  19. 19.
    Jeong, J.Y., et al.: An efficient re-coloring method with information preserving for the color-blind. IEEE Trans. Consum. Electron. 57(4), 1953–1960 (2011)CrossRefGoogle Scholar
  20. 20.
    Buades, A., et al.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65 (2005)Google Scholar
  21. 21.
    Ishihara, S.: Ishihara’s Test for Colour-Blindness. Kanehara Shuppan Company, Tokyo (1985)Google Scholar
  22. 22.
    Chen, X., et al.: Method and eyeglasses for rectifying color blindness. U.S. Patent 5,369,453 (1994)Google Scholar
  23. 23.
    Melillo, P., et al.: Wearable improved vision system for color vision deficiency correction. IEEE J. Transl. Eng. Health Med. 5, 1–7 (2017)CrossRefGoogle Scholar
  24. 24.
    Wing, T.: Colorblind vehicle driving aid. U.S. Patent Application 10/799,112 (2005)Google Scholar
  25. 25.
    Hu, M., et al.: An overview of assistive devices for blind and visually impaired people. Int. J. Robot. Autom. 34(5), 580–598 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
  2. 2.Hangzhou Hikvision Digital Technology Co., Ltd.HangzhouChina
  3. 3.Shanghai Police CollegeShanghaiChina

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