Advertisement

Color Correction for Stereoscopic Images Based on Gradient Preservation

  • Pengyu Liu
  • Yuzhen NiuEmail author
  • Junhao Chen
  • Yiqing Shi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 997)

Abstract

Color correction can eliminate the color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local color correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent color correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large color difference.

Keywords

SIFT feature match Color correction Region mapping Gradient preservation 

Notes

Acknowledgments

The presented research work is supported by the National Natural Science Foundation of China under Grant 61672158, Grant 61671152, and Grant 61502105, in part by the Fujian Natural Science Funds for Distinguished Young Scholar under Grant 2015J06014, in part by the Technology Guidance Project of Fujian Province under Grant No. 2017H0015, and in part by the Fujian Collaborative Innovation Center for Big Data Application in government.

References

  1. 1.
    Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics 21(5), 34–41 (2002)Google Scholar
  2. 2.
    Wang, Q., Yan, P., Yuan, Y., Li, X.: Robust color correction in stereo vision. In: IEEE International Conference on Image Processing, pp. 965–968 (2011)Google Scholar
  3. 3.
    Pitie, F., Kokaram, A.C., Dahyot, R.: N-dimensional probability density function transfer and its application to colour transfer. In: IEEE International Conference on Computer Vision, pp. 1434–1439 (2005)Google Scholar
  4. 4.
    Piti, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Comput Vis. Image Underst. 107(1C2), 123–137 (2007)Google Scholar
  5. 5.
    Zheng, X., Niu, Y., Chen, J., Chen, Y.: Color correction for stereo-scopic image based on matching and optimization. In International Conference on 3D Immersion, pp. 1–8 (2017)Google Scholar
  6. 6.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. 33(5), 978–994 (2011)CrossRefGoogle Scholar
  7. 7.
    Xiao, X., Ma, L.: Color transfer in correlated color space. In: International Conference on Virtual Reality Continuum, pp. 305–309 (2006)Google Scholar
  8. 8.
    Zhang, M., Georganas, N.D.: Fast color correction using principal regions mapping in different color spaces. Real-Time Imaging 10(1), 23–30 (2004)CrossRefGoogle Scholar
  9. 9.
    Fecker, U., Barkowsky, M., Kaup, A.: Histogram-based prefiltering for luminance and chrominance compensation of multiview video. IEEE Trans. on Circuits Syst. Video Technol. 18(9), 1258–1267 (2008)CrossRefGoogle Scholar
  10. 10.
    Xiao, X., Ma, L.: Gradient-Preserving Color Transfer. Comput. Graphics Forum 28, 1879–1886 (2009).  https://doi.org/10.1111/j.1467-8659.2009.01566.x
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  12. 12.
    Niu, Y., Zhang, H., Guo, W., Ji, R.: Image quality assessment for color correction based on color contrast similarity and color value difference. IEEE Trans. on Circuits Syst. Video Technol. 28(4), 849–862 (2018)Google Scholar
  13. 13.
    Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Trans. Image Processing A Publication of the IEEE Signal Processing Society 20(8), 2378 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Zhang, L., Shen, Y., Li, H.: Vsi: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10), 4270–4281 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006 (2010)CrossRefGoogle Scholar
  16. 16.
    Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Lee, D., Plataniotis, K.N.: Towards a full-reference quality assessment for color images using directional statistics. IEEE Trans. Image Process. 24(11), 3950–3965 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Preiss, J., Fernandes, F., Urban, P.: Color-image quality assessment: from prediction to optimization. IEEE Trans. Image Process. 23(3), 1366–1378 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
  20. 20.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  21. 21.
    Chang, H.-W., Zhang, Q.-W., Wu, Q.-G., Gan, Y.: Perceptual image quality assessment by independent feature detector. Neurocomputing 151, 1142–1152, March (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pengyu Liu
    • 1
  • Yuzhen Niu
    • 1
    • 2
    Email author
  • Junhao Chen
    • 1
  • Yiqing Shi
    • 1
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Fujian Provincial Key Lab of the Network Computing and Intelligent Information ProcessingFujianChina

Personalised recommendations