Journal of Real-Time Image Processing

, Volume 16, Issue 1, pp 115–125 | Cite as

Implementing real-time RCF-Retinex image enhancement method using CUDA

  • Xiaomin Yang
  • Lihua Jian
  • Wei Wu
  • Kai Liu
  • Binyu Yan
  • Zhili Zhou
  • Jian PengEmail author
Special Issue Paper


RCF-Retinex is a novel Retinex-based image enhancement method which can improve contrast, eliminate noise, and enhance details simultaneously. It utilizes region covariance filter (RCF) to estimate the illumination. However, RCF-Retinex encounters time-consuming problem, since the region covariance filter is computationally intensive, which restricts the practical application in real-time systems. Therefore, it is necessary to decrease the computational complexity by parallelization. This paper proposes a GPU-based RCF-Retinex, which can accelerate region covariance filter using CUDA. It is feasible to use CUDA to parallel the region covariance filter due to its consecutive convolution operations, thus we can obtain the illumination image fast. Experiments have proved the improvement of running time and the enhancement results are similar with those using the unaccelerated RCF-Retinex method.


RCF-Retinex CUDA GPU Accelerating 


  1. 1.
    Ahn, H., Keum, B., Kim, D., Lee, H.S.: Adaptive local tone mapping based on retinex for high dynamic range images. IEEE Int. Conf. Consum. Electron. (2013). Google Scholar
  2. 2.
    Alspach, D.L., Sorenson, H.W.: Nonlinear Bayesian estimation using gaussian sum approximations. IEEE Trans. Autom. Control 17(4), 439–448 (1972)CrossRefzbMATHGoogle Scholar
  3. 3.
    Choudhury, A., Medioni, G.: Perceptually motivated automatic color contrast enhancement. In: ICCV 2009—CRICV workshop 7525(1), 1893–1900 (2009)Google Scholar
  4. 4.
    Fuyu Tao, X.Y.: Retinex-based image enhancement framework by using region covariance filter. Soft Comput. (2017).
  5. 5.
    Gembris, D., Neeb, M., Gipp, M., Kugel, A.: Correlation analysis on GPU systems using Nvidia’s CUDA. J. Real-Time Image Proc. 6(4), 275–280 (2011)CrossRefGoogle Scholar
  6. 6.
    Jang, B., Schaa, D., Mistry, P., Kaeli, D.: Exploiting memory access patterns to improve memory performance in data-parallel architectures. IEEE Trans. Parallel Distrib. Syst. 22(1), 105–118 (2011)CrossRefGoogle Scholar
  7. 7.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–62 (1997)CrossRefGoogle Scholar
  8. 8.
    Jobson, D.J., Rahman, Z.U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)CrossRefGoogle Scholar
  9. 9.
    Karacan, L., Erdem, E., Erdem, A.: Structure-preserving image smoothing via region covariances. ACM Trans. Graph. 32(6), 1–11 (2013)CrossRefGoogle Scholar
  10. 10.
    Land, E.H., Mccann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)CrossRefGoogle Scholar
  11. 11.
    Rahman, Z.U., Jobson, D.J., Woodell, G.A.: Multi-scale retinex for color image enhancement. IEEE Int. Conf. Image. Proc. 3, 1003–1006 (1996). CrossRefGoogle Scholar
  12. 12.
    Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Computer Vision—ECCV 2006, European Conference on Computer Vision, Graz, Proceedings, 7–13 May 2006, pp 589–600 (2006)Google Scholar
  13. 13.
    Wang, Y., Wang, H., Yin, C., Dai, M.: Biologically inspired image enhancement based on retinex. Neurocomputing 177, 373–384 (2016)CrossRefGoogle Scholar
  14. 14.
    Wang, Y.K., Huang, W.B.: A CUDA-enabled parallel algorithm for accelerating retinex. Springer, New York (2014)CrossRefGoogle Scholar
  15. 15.
    Wu, J., Deng, L., Jeon, G.: Image autoregressive interpolation model using GPU-parallel optimization. IEEE Trans. Ind. Inform. PP(99), 1 (2017)Google Scholar
  16. 16.
    Yang, Z., Zhu, Y., Pu, Y.: Parallel image processing based on CUDA. IEEE Proc. Int. Conf. Comp. Sci. Software. Eng. 3, 198–201 (2008). Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiaomin Yang
    • 1
  • Lihua Jian
    • 1
  • Wei Wu
    • 1
  • Kai Liu
    • 3
  • Binyu Yan
    • 1
  • Zhili Zhou
    • 4
  • Jian Peng
    • 2
    Email author
  1. 1.College of Electronics and Information EngineeringSichuan UniversityChengduChina
  2. 2.College of Computer ScienceSichuan UniversityChengduChina
  3. 3.School of Electrical Engineering and InformationSichuan UniversityChengduChina
  4. 4.Jiangsu Engineering Center of Network Monitoring and School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations