The Visual Computer

, Volume 35, Issue 5, pp 695–705 | Cite as

Correction of overexposure utilizing haze removal model and image fusion technique

  • Chenwei YangEmail author
  • Huajun Feng
  • Zhihai Xu
  • Qi Li
  • Yueting Chen
Original Article


This paper presents an efficient method for overexposure correction utilizing haze removal model and image fusion technique, which draws on the experience of HDR technique. Assuming an OE image can be modeled as a normal exposure image added up with a layer of asymmetrical colorful haze, its submerged information in OE regions is enhanced by an improved haze removal model based on dark channel prior. The enhancement result possesses better visualization in OE regions and color distortion to a certain extent. With the image fusion technique based on weighted least squares filters and global contrast-based saliency, the texture obtained in OE regions is utilized to restore the overexposure. The advantages of the selected image fusion technique are validated in the paper. In the experiments, the proposed method is compared with conventional methods to corroborate the performance. Both the subjective visualization and quantitative indicators show that the result is effective in correcting the overexposure without increasing pseudo-information and oversaturation.


Overexposure Image restoration Dark channel prior Weighted least squares filter Image fusion 


  1. 1.
    Aggarwal, M., Ahuja, N.: Split aperture imaging for high dynamic range. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001, pp. 10–17. IEEE (2001)Google Scholar
  2. 2.
    Tumblin, J., Agrawal, A., Raskar, R.: Why I want a gradient camera. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, pp. 103–110. IEEE (2005)Google Scholar
  3. 3.
    Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. In: ACM Transactions on Graphics (TOG), vol. 3, pp. 257–266. ACM (2002)Google Scholar
  4. 4.
    Hasinoff, S.W., Durand, F., Freeman, W.T.: Noise-optimal capture for high dynamic range photography. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 553–560. IEEE (2010)Google Scholar
  5. 5.
    Jobson, D.J., Rahman, Z., 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
  6. 6.
    Masood, S.Z., Zhu, J., Tappen, M.F.: Automatic correction of saturated regions in photographs using cross–channel correlation. In: Computer Graphics Forum 2009, vol. 7, pp. 1861–1869. Wiley Online Library (2009)Google Scholar
  7. 7.
    Guo, D., Cheng, Y., Zhuo, S., Sim, T.: Correcting over-exposure in photographs. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 515–521. IEEE (2010)Google Scholar
  8. 8.
    Lee, D.-H., Yoon, Y.-J., Kang, S., Ko, S.-J.: Correction of the overexposed region in digital color image. IEEE Trans. Consum. Electron. 60(2), 173–178 (2014)CrossRefGoogle Scholar
  9. 9.
    Hou, L., Ji, H., Shen, Z.: Recovering over-/underexposed regions in photographs. SIAM J. Imag. Sci. 6(4), 2213–2235 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Yoon, Y.-J., Lee, D.-H., Kang, S.-J., Park, W.-J., Ko, S.-J.: Patch-based over-exposure correction in image. In: The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), pp. 1–3. IEEE (2014)Google Scholar
  11. 11.
    Arora, S., Hanmandlu, M., Gupta, G., Singh, L.: Enhancement of overexposed color images. In: 2015 3rd International Conference on Information and Communication Technology (ICoICT), pp. 207–211. IEEE (2015)Google Scholar
  12. 12.
    Abebe, M.A., Booth, A., Kervec, J., Pouli, T., Larabi, M.-C.: Towards an automatic correction of over-exposure in photographs: application to tone-mapping. Comput. Vis. Image Underst. (2017).
  13. 13.
    Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.N.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kim, J.-H., Jang, W.-D., Sim, J.-Y., Kim, C.-S.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)CrossRefGoogle Scholar
  15. 15.
    Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  16. 16.
    Fattal, R.: Single image dehazing. ACM Trans Gr (TOG) 27(3), 72 (2008)Google Scholar
  17. 17.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  18. 18.
    Park, D., Han, D.K., Ko, H.: Single image haze removal with WLS-based edge-preserving smoothing filter. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2469–2473. IEEE (2013)Google Scholar
  19. 19.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, 1998, pp. 839–846. IEEE (1998)Google Scholar
  20. 20.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  21. 21.
    Gastal, E.S., Oliveira, M.M.: Domain transform for edge-aware image and video processing. In: ACM Transactions on Graphics (ToG), vol. 4, p. 69. ACM (2011)Google Scholar
  22. 22.
    Shen, C.T., Chang, F.J., Hung, Y.P., Pei, S.C.: Edge-preserving image decomposition using L1 fidelity with L0 gradient. In: SIGGRAPH Asia 2012 Technical Briefs 2012, pp. 1–4 (2012)Google Scholar
  23. 23.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. In: ACM Transactions on Graphics (TOG), vol. 3, p. 67. ACM (2008)Google Scholar
  24. 24.
    Kim, Y., Min, D., Ham, B., Sohn, K.: Fast domain decomposition for global image smoothing. IEEE Trans. Image Process. 26, 4079–4091 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Chen, S.-D., Ramli, A.R.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49(4), 1301–1309 (2003)CrossRefGoogle Scholar
  26. 26.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV 1994, pp. 474–485. Academic Press Professional, Inc, CambridgeGoogle Scholar
  27. 27.
    Yoon, Y.-J., Byun, K.-Y., Lee, D.-H., Jung, S.-W., Ko, S.-J.: A new human perception-based over-exposure detection method for color images. Sensors 14(9), 17159–17173 (2014)CrossRefGoogle Scholar
  28. 28.
    Panetta, K.A., Wharton, E.J., Agaian, S.S.: Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans. Syst. Man Cybern., Part B (Cybern.) 38(1), 174–188 (2008)CrossRefGoogle Scholar
  29. 29.
    Michelson, A.A.: Studies in Optics. Courier Corporation, North Chelmsford (1995)zbMATHGoogle Scholar
  30. 30.
    Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color–difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30(1), 21–30 (2005)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.State Key Laboratory of Modern Optical InstrumentationZhejiang UniversityZhejiangChina

Personalised recommendations