Enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters

  • Tunai Porto Marques
  • Alexandra Branzan Albu
  • Maia Hoeberechts
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11188)


Low levels of lighting in images and videos may lead to poor results in segmentation, detection, tracking, among numerous other computer vision tasks. Deep-sea camera systems, such as those deployed on the Ocean Networks Canada (ONC) cabled ocean observatories, use artificial lighting to illuminate and capture videos of deep-water biological environments. When these lighting systems fail, the resulting images become hard to interpret or even completely useless because of their lighting levels. This paper proposes an effective framework to enhance the lighting levels of underwater images, increasing the number of visible, meaningful features. The process involves the dehazing of images using a dark channel prior and fast guided filters.


Image dehazing Low-lighting underwater imagery Dark channel prior Transmission map refinement Fast guided filter 


  1. 1.
    Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, Barcelona, pp. 1–6 (2011)Google Scholar
  2. 2.
    Schechnner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Optics 42(3), 511–525 (2003)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Tarel, J.P., Hautière, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, Kyoto (2009)Google Scholar
  6. 6.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision, Sydney, NSW, pp. 617–624 (2013)Google Scholar
  7. 7.
    Fattal, R.: Dehazing using color lines. ACM Trans. Graph. 34, 1–14 (2014)CrossRefGoogle Scholar
  8. 8.
    Ancuti, C., Ancuti, C.O., Vleeschouwer, C.: D-Hazy: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE International Conf. on Image Processing (2016)Google Scholar
  9. 9.
    Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision – ECCV 2016, pp 154–169, September 2016CrossRefGoogle Scholar
  10. 10.
    Alharbi, E.B., Ge, P., Wang, H.: A research on single image dehazing algorithms based on dark channel prior. J. Comput. Commun. 4, 47–55 (2016)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.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846 (1988)Google Scholar
  13. 13.
    He, K., Sun, J.: Fast Guided Filter. eprint arXiv:1505.00996. Bibliographic code: 2015arXiv150500996H, May 2015
  14. 14.
    Lee, S., Yun, S., Nam, J., Won, C.S., Jung, S.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 2016, 4 (2016)CrossRefGoogle Scholar
  15. 15.
    Tarel, J.-P., et al.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)CrossRefGoogle Scholar
  16. 16.
    Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014). Scholar
  17. 17.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). Scholar
  18. 18.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)CrossRefGoogle Scholar
  19. 19.
    Anaya, J., Barbu, A.: RENOIR – a dataset for real low-light image noise reduction. J. Vis. Commun. Image Represent. 51(2), 144–154 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of VictoriaVictoriaCanada
  2. 2.Ocean Networks CanadaVictoriaCanada

Personalised recommendations