Skip to main content
SpringerLink
Log in

DLT-Net: deep learning transmittance network for single image haze removal

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Outdoor images taken in inclement weather conditions are often contaminated with colloidal particles and droplet in the atmosphere. These captured images are susceptible to low contrast, poor visibility, and color distortion, which is the reason for serious errors in digital image vision systems. Therefore, defogging research has material significance for practical applications. In this paper, image dehazing is regarded as a mathematical inversion and image restoration process on the basis of fog image degradation model. The global atmospheric light A can be approximately estimated by combining Gaussian low-pass filtering with the single-threshold segmentation and binary tree method. And a deep learning transmittance network is adopted to modify transmittance. Comparison experimental results show that our method is effective in dealing with thick fog, complex scenes and multicolor images. In addition, our method is superior to four other state-of-the-art defogging methods in visual impact, universality and running speed.

Graphic abstract

The overall framework of our method

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: Proceedings of the IEEE CVPR00, pp. 598–605. IEEE Computer Society, Washington (2000)

  2. Ru, Y., Tanaka, G.: Proposal of multiscale retinex using illumination adjustment for digital images. Ieice Trans. Fundam. Electron. Commun. Comput. Sci. E99(11), 2003–2007 (2016)

    Google Scholar 

  3. Raffei, A.M., Asmuni, H., Hassan, R., Othman, R.M.: A low lighting or contrast ratio visible iris recognition using iso-contrast limited adaptive histogram equalization. Knowl. Based Syst. 74(1), 40–48 (2015)

    Google Scholar 

  4. Wang, Z., Feng, Y.: Fast single haze image enhancement. Comput. Electr. Eng. 40(3), 785–795 (2014)

    Google Scholar 

  5. Babu, P., Rajamani, V.: Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images. Int. J. Imaging Syst. Technol. 25(1), 24–32 (2015)

    Google Scholar 

  6. Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)

    Google Scholar 

  7. Seow, M.J., Asari, V.K.: Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing 69(7), 954–958 (2006)

    Google Scholar 

  8. Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: IEEE International Conference on Computational Photography, pp. 1–11 (2014)

  9. Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 1–9 (2008)

    Google Scholar 

  10. He, K., Sun, J., Tang, X.O.: Single image haze removal using dark channel prior. In: Proceedings of the IEEE CVPR09, pp. 1956–1963. IEEE Computer Society, Washington (2009)

  11. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)

    Google Scholar 

  12. Fu, H., Wu, B., Shao, Y., Zhang, H.: Perception oriented haze image definition restoration by basing on physical optics model. IEEE Photonics J. 10(3), 1–16 (2018)

    Google Scholar 

  13. Fu, H., Wu, B., Zhang, H.: De-hazing of atmosphere veil haze image. Opt. Precis. Eng. 24(8), 2018–2026 (2016)

    Google Scholar 

  14. Li, J., Li, G., Fan, H.: Image dehazing using residual-based deep CNN. IEEE Access. 6, 26831–26842 (2015)

    Google Scholar 

  15. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single Image Dehazing via Multi-scale Convolutional Neural Networks. In: European Conference on Computer Vision, pp. 154–169 (2016)

  16. Koschmider, H.: Theorie der horizontalen sichtweite Contrib. Beitr. Phys. Freien Atmos. 12, 171–181 (1924)

    Google Scholar 

  17. Li, Z., Zheng, J.: Edge-preserving decomposition based single image haze removal. IEEE Trans. Image Process. 24(12), 5432–5441 (2015)

    MathSciNet  MATH  Google Scholar 

  18. Seonhee, P., Soohwan, Y., Minseo, K., Kwanwoo, P., Joonki, P.: Dual autoencoder network for retinex-based low-light image enhancement. IEEE Access 6, 22084–22093 (2018)

    Google Scholar 

  19. Fu, H., Wu, B., Shao, Y., Zhang, H.: Scene-awareness based single image Dehazing technique via automatic estimation of sky area. IEEE Access 7, 1829–1839 (2019)

    Google Scholar 

  20. Fu, H., Wu, B., Zhang, H., Xu, J.: Fast image dehazing based on dark-Chanel physical model. Opto-Electron. Eng. 2, 82–88 (2016)

    Google Scholar 

  21. Veelaert, P.: Image segmentation with adaptive region growing based on a polynomial surface model. J. Electron. Imaging 22(4), 3004 (2013)

    Google Scholar 

  22. Ancuti, C.O., Ancuti, C., Hermans, C., Philippe, B.: A fast semi-inverse approach to detect and remove the haze from a single image. In: 2010-10th Asian Conference on Computer Vision. Queenstown (ACCV), pp. 501–514(2010)

  23. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)

    MathSciNet  MATH  Google Scholar 

  24. Li, X.: Preconditioned stochastic gradient descent. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1454–1466 (2018)

    MathSciNet  Google Scholar 

  25. Wang, K., Wang, H., Li, Y., Hu, Y., Li, Y.: Quantitative performance evaluation for Dehazing algorithms on synthetic outdoor hazy images. IEEE Access 6(99), 20481–20496 (2018)

    Google Scholar 

  26. Wang, W., Yuan, X., Wu, X., Liu, Y.: Fast image dehazing method based on linear transformation. IEEE Trans. Multimed. 19(6), 1142–1154 (2017)

    Google Scholar 

  27. Luan, Z., Shang, Y., Zhou, X., Shao, Z., Guo, D., Liu, X.: Fast single image dehazing based on a regression model. Neurocomputing 245, 10–22 (2017)

    Google Scholar 

  28. Hautiere, N., Tarel, J.P., Aubert, D.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2011)

    MathSciNet  MATH  Google Scholar 

  29. Ju, M., Gu, Z., Zhang, D.: Single image haze removal based on the improved atmospheric scattering model. Neurocomputing 260, 180–191 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chongqing Chongyou Information Technology Design Co., Ltd, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Bin Li

  2. China Mobile Communications Group Chongqing Co., Ltd, Chongqing, 400044, China

    Jingjuan Zhao

  3. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China

    Hui Fu

Authors
  1. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingjuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, B., Zhao, J. & Fu, H. DLT-Net: deep learning transmittance network for single image haze removal. SIViP 14, 1245–1253 (2020). https://doi.org/10.1007/s11760-020-01665-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01665-9

Keywords

Search

Navigation