Robust Image and Video Dehazing with Visual Artifact Suppression via Gradient Residual Minimization

  • Chen ChenEmail author
  • Minh N. Do
  • Jue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)


Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of Li et al. (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips.


Video dehazing Image dehazing Contrast enhancement Artifact suppression 

Supplementary material (62.4 mb)
Supplementary material 1 (zip 63850 KB)


  1. 1.
    Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18(11), 2419–2434 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bonneel, N., Tompkin, J., Sunkavalli, K., Sun, D., Paris, S., Pfister, H.: Blind video temporal consistency. ACM Trans. Graph. 34(6), 196 (2015)CrossRefGoogle Scholar
  3. 3.
    Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM J. Imaging Sci. 3(3), 492–526 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40(1), 120–145 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Chen, C., Li, Y., Liu, W., Huang, J.: Image fusion with local spectral consistency and dynamic gradient sparsity. In: CVPR, pp. 2760–2765 (2014)Google Scholar
  6. 6.
    Fattal, R.: Single image dehazing. ACM Trans. Grap. 27, 72 (2008)Google Scholar
  7. 7.
    Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34(1), 13 (2014)CrossRefGoogle Scholar
  8. 8.
    Ferstl, D., Reinbacher, C., Ranftl, R., Rüther, M., Bischof, H.: Image guided depth upsampling using anisotropic total generalized variation. In: ICCV, pp. 993–1000 (2013)Google Scholar
  9. 9.
    Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmío, M.: Enhanced variational image dehazing. SIAM J. Imaging Sci. 8(3), 1519–1546 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  12. 12.
    Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. 27, 116 (2008)CrossRefGoogle Scholar
  13. 13.
    Koschmieder, H.: Theorie der horizontalen Sichtweite. In: Beitrge zur Physik der freien Atmosphre (1924)Google Scholar
  14. 14.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Chen, C., Yang, F., Huang, J.: Deep sparse representation for robust image registration. In: CVPR, pp. 4894–4901 (2015)Google Scholar
  16. 16.
    Li, Y., Guo, F., Tan, R.T., Brown, M.S.: A contrast enhancement framework with JPEG artifacts suppression. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 174–188. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Li, Z., Tan, P., Tan, R.T., Zou, D., Zhiying Zhou, S., Cheong, L.F.: Simultaneous video defogging and stereo reconstruction. In: CVPR, pp. 4988–4997 (2015)Google Scholar
  18. 18.
    Lu, S., Ren, X., Liu, F.: Depth enhancement via low-rank matrix completion. In: CVPR, pp. 3390–3397 (2014)Google Scholar
  19. 19.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: ICCV, pp. 617–624 (2013)Google Scholar
  20. 20.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  21. 21.
    Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: IEEE Intelligent Vehicles Symposium, pp. 401–407 (2012)Google Scholar
  22. 22.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1–3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  23. 23.
    Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: CVPR, vol. 1, pp. I–325 (2001)Google Scholar
  24. 24.
    Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation. In: CVPR, vol. 2, pp. 1984–1991 (2006)Google Scholar
  25. 25.
    Tan, R.T.: Visibility in bad weather from a single image. In: CVPR, pp. 1–8 (2008)Google Scholar
  26. 26.
    Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: CVPR, pp. 2995–3002 (2014)Google Scholar
  27. 27.
    Tarel, J.P., Hautière, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)CrossRefGoogle Scholar
  28. 28.
    Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. In: BMVC, vol. 1, p. 3 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Adobe ResearchSeattleUSA

Personalised recommendations