Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1831–1856 | Cite as

Visibility dehazing based on channel-weighted analysis and illumination tuning

  • Jung-San LeeEmail author
  • Chih-Hsuan Li
  • Hsin-Yu Lee
Article
  • 42 Downloads

Abstract

The air pollution and foggy weather often result in serious distortion while taking photos or recognizing patterns. He et al. have introduced the dark channel prior to solve this dehazing problem. Unfortunately, it cannot function well once the color difference of target image is large. More precisely, the dehazed result looks unnatural. Thus, we aim to develop a brand-new visibility dehazing technique based on the channel-weighted analysis and illumination tuning. The channel-weighted analysis is adopted to eliminate the unnatural effect, while the illumination tuning is applied to refine the details. Simulation results have demonstrated that the new method can guarantee the readability of a hazed image after removing noise, including the foggy photo and sandstorm one.

Keywords

Visibility Dehaze Dark channel Illumination Fog Sandstorm 

Notes

References

  1. 1.
    Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1674–1682Google Scholar
  2. 2.
    Berman D, Treibitz T, Avidan S (2017) Air-light estimation using haze-lines, Proceedings of IEEE Conference on Computational Photography (ICCP), pp.1–9, MayGoogle Scholar
  3. 3.
    He KM, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353CrossRefGoogle Scholar
  4. 4.
    Huang SC, Chen BH, Wang WJ (2014) Visibility restoration of single hazy images captured in real-world weather. IEEE Transactions on Circuits and System for Video Technology 24(10):1814–1824CrossRefGoogle Scholar
  5. 5.
    Huang Y, Yao H, Zhao S, Zhang Y (2017) Towards more efficient and flexible face image deblurring using robust salient face landmark detection. Multimedia Tools and Applications 76(1):123–142CrossRefGoogle Scholar
  6. 6.
    Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search, Proceedings of 10th European Conference on Computer, pp. 304–317Google Scholar
  7. 7.
    Jian M, Dong J, Lam KM (2013) FSAM: a fast self-adaptive method for correcting non-uniform illumination for 3D reconstruction. Comput Ind 64(9):1229–1236CrossRefGoogle Scholar
  8. 8.
    Jian M, Lam KM, Dong J (2014) Illumination-insensitive texture discrimination based on illumination compensation and enhancement. Comput Ind 269:60–72MathSciNetGoogle Scholar
  9. 9.
    Jian M, Yin Y, Dong J, Zhang W (2018) Comprehensive assessment of non-uniform illumination for 3D heightmap reconstruction in outdoor environments. Comput Ind 99:110–118CrossRefGoogle Scholar
  10. 10.
    Jiang X, Yao H, Zhao S (2017) Text image deblurring via two-tone prior. Neurocomputing 242:1–14CrossRefGoogle Scholar
  11. 11.
    Kopf J, Neubert B, Chen B, Cohen M, Or DC, Deussen O, Uyttendaele M, Lischinski D (2008) Deep photo: Model-based photograph enhancement and viewing, ACM Transactions on Graphics (TOG), 27(5), Article 116Google Scholar
  12. 12.
    Meng GF, Wang Y, Duan J, Xiang S, and Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization, Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 617–624Google Scholar
  13. 13.
    Narasimhan SG, Nayar SK (2001) Removing weather effects from monochrome images. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2:186–193Google Scholar
  14. 14.
    Narasimhan SG, Nayar SK (2003) Interactive (De) weathering of an image using physical models, IEEE Workshop on Color and Photometric Methods in Computer Vision (ICCV), pp. 1387–1394Google Scholar
  15. 15.
    Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724CrossRefGoogle Scholar
  16. 16.
    Schechner YY, Narasimhan SG, Nayar SK (2003) Polarization based vision through haze. Appl Opt 42(3):511–525CrossRefGoogle Scholar
  17. 17.
    Tan R (2008) Visibility in bad weather from a single image, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8Google Scholar
  18. 18.
    Tan K, Oakley JP (2000) Enhancement of color images in poor visibility conditions. Proceedings of IEEE International Conference on Signal Processing (ICIP) 2:788–791Google Scholar
  19. 19.
    Zhu M, He B, Wu Q (2018) Single image dehazing based on dark channel prior and energy minimization. IEEE Signal Processing Letters 25(2):174–178CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan

Personalised recommendations