Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Single image dehazing algorithm based on sky segmentation and optimal transmission maps

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

The majority of existing physical model-based dehazing algorithms have problems, including color distortion and halo effects, when restoring hazy outdoor scenes that often contain large areas of the sky. This paper proposes a single image dehazing algorithm based on sky segmentation and an optimal transmission map to improve the quality of dehazed images containing sky regions. The proposed algorithm acquires the sky region of a hazy image by using the mean shift technique and prior information of sky color rules and estimates the atmospheric light by introducing adaptive threshold constraints based on the sky region. Next, a hazy image feature-based objective function is designed, and a transmission map is accurately estimated by introducing gradient domain-guided filtering. On this basis, images are restored with the atmospheric scattering model, and the final dehazed images are obtained through tone adjustment. The experimental results demonstrate that the proposed algorithm is robust and can effectively eliminate haze and enrich the edge details of images. Compared to other algorithms, the saturated pixel ratio of the present algorithm is approximately zero, indicating the preferable color saturation of the restored images.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. https://sites.google.com/view/reside-dehaze-datasets.

References

  1. Jin, J., Zhang, J., Shao, F., Lyu, Z., Wang, D.: A novel ocean bathymetry technology based on an unmanned surface vehicle. Acta Oceanol. Sin. 37(9), 99–106 (2018)

    Article  Google Scholar 

  2. Zhang, T., Han, Q., El-Lat, A.A.A., Bai, X., Niu, X.: 2-D cartoon character detection based on scalable-shape context and hough voting. Inf. Technol. J. 12(12), 2342–2349 (2013)

    Article  Google Scholar 

  3. Shiau, Y., Kuo, Y., Chen, P., Hsu, F.: Vlsi design of an efficient flicker-free video defogging method for real-time applications. IEEE Trans. Circuits Syst. Video Technol. 29(1), 238–251 (2019)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Raikwar, S.C., Tapaswi, S.: Tight lower bound on transmission for single image dehazing. Vis. Comput. 36, 191–209 (2020)

    Article  MATH  Google Scholar 

  7. Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  8. Shin, J., Kim, M., Paik, J., Lee, S.: Radiance-reflectance combined optimization and structure-guided l0-norm for single image dehazing. IEEE Trans. Multimedia 22(1), 30–44 (2020)

    Article  Google Scholar 

  9. Zhao, D., Xu, L., Yan, Y., et al.: Multi-scale optimal fusion model for single image dehazing. Signal Process. Image Commun. 74, 253–265 (2019)

    Article  Google Scholar 

  10. Salazar-Colores, S., Cabal-Yepez, E., Ramos-Arreguin, J.M., Botella, G., Ledesma-Carrillo, L.M., Ledesma, S.: A fast image dehazing algorithm using morphological reconstruction. IEEE Trans. Image Process. 28(5), 2357–2366 (2019)

    Article  MathSciNet  Google Scholar 

  11. Zhu, Y., Tang, G., Zhang, X., et al.: Haze removal method for natural restoration of images with sky. Neurocomputing 275(31), 499–510 (2017)

    Google Scholar 

  12. Salazar-Colores, S., Moya-Sánchez, E.U., Ramos-Arreguín, J.M., Cabal-Yépez, E., Flores, G., Cortés, U.: Fast single image defogging with robust sky detection. IEEE Access 8, 149176–149189 (2020)

    Article  Google Scholar 

  13. Liu, J., Huang, B., Wei, G.: A fast effective single image dehazing algorithm. Acta Electron. Sin. 45(8), 1896–1901 (2017)

    Google Scholar 

  14. Raikwar, S.C., Tapaswi, S.: Lower bound on transmission using non-linear bounding function in single image dehazing. IEEE Trans. Image Process. 29, 4832–4847 (2020)

    Article  MATH  Google Scholar 

  15. Xiao, C., Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28, 713–721 (2012)

    Article  Google Scholar 

  16. 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)

    Article  MathSciNet  MATH  Google Scholar 

  17. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: AOD-Net: all-in-one dehazing network. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4780–4788 (2017).

  18. Ren, W., Liu, S., et al.: Single image dehazing via multi-scale convolutional neural networks. In: ECCV (2016).

  19. Yuan, K., Wei, J., Lu, W., Xiong, N.: Single image dehazing via nin-dehazenet. IEEE Access 7, 181348–181356 (2019)

    Article  Google Scholar 

  20. Kim, J.H., Jang, W.D., Sim, J.Y., et al.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)

    Article  Google Scholar 

  21. He, L., Zhao, J., Zheng, N., Bi, D.: Haze removal using the difference- structure-preservation prior. IEEE Trans. Image Process. 26(3), 1063–1075 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ngo, D., Lee, S., Nguyen, Q.H., et al.: Single image haze removal from image enhancement perspective for real-time vision-based systems. Sensors 20(18), 5170–5191 (2020)

    Article  Google Scholar 

  23. Liu, X., Li, H., Zhu, C.: Joint contrast enhancement and exposure fusion for real-world image dehazing. IEEE Trans. Multimedia (2021). https://doi.org/10.1109/TMM.2021.3110483

    Article  Google Scholar 

  24. Fang, Z., Zhao, M., Yu, Z., et al.: A guiding teaching and dual adversarial learning framework for a single image dehazing. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02184-5

    Article  Google Scholar 

  25. Zhang, S., He, F.: DRCDN: learning deep residual convolutional dehazing networks. Vis. Comput. 36, 1797–1808 (2020)

    Article  Google Scholar 

  26. Zhao, S., Zhang, L., Shen, Y., Zhou, Y.: RefineDNet: a weakly supervised refinement framework for single image dehazing. IEEE Trans. Image Process. 30, 3391–3404 (2021)

    Article  Google Scholar 

  27. Chen, Z., Hu, Z., Sheng, B., et al.: Simplified non-locally dense network for single-image dehazing. Vis. Comput. 36, 2189–2200 (2020)

    Article  Google Scholar 

  28. Kuanar, S., Mahapatra, D., Bilas, M., et al.: Multi-path dilated convolution network for haze and glow removal in nighttime images. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02071-z

    Article  Google Scholar 

  29. Agrawal, S.C., Jalal, A.S.: Distortion-free image dehazing by superpixels and ensemble neural network. Vis. Comput. (2021). https://doi.org/10.1007/s00371-020-02049-3

    Article  Google Scholar 

  30. Ngo, D., Lee, S., Kang, B.: Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sensing 12(14), 2233–2248 (2020)

    Article  Google Scholar 

  31. Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  32. Zhang, T., El-Latif, A., Ning, W., et al.: A new image segmentation method via fusing NCut eigenvectors maps. In: Proceedings of the Fourth International Conference on Digital Image Processing (2012). https://doi.org/10.1117/12.956472

  33. Lei, T., Liu, P., Jia, X., Zhang, X., Meng, H., Nandi, A.K.: Automatic fuzzy clustering framework for image segmentation. IEEE Trans. Fuzzy Syst. 28(9), 2078–2092 (2020)

    Article  Google Scholar 

  34. Lei, T., Jia, X., Zhang, Y., Liu, S., Meng, H., Nandi, A.K.: Superpixel-based fast fuzzy c-means clustering for color image segmentation. IEEE Trans. Fuzzy Syst. 27(9), 1753–1766 (2019)

    Article  Google Scholar 

  35. Comaniciu, D., Meer, P.: Robust analysis of feature spaces: color image segmentation. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750–755. IEEE (1997).

  36. Battiato, S., Curti, S., La, M.C., Tortora, M., Scordato, E.: Depth map generation by image classification. Proc. SPIE 5302, 95–104 (2004)

    Article  Google Scholar 

  37. Yang, A., Xing, J., Liu, J., Li, X.: Single image dehazing based on middle channel compensation. J. Northeast. Univ. Nat. Sci. 42(2), 180–188 (2021)

    Google Scholar 

  38. Shi, Z., Long, J., Tang, W., et al.: Single image dehazing in inhomogeneous atmosphere. Optik Int. J. Light Electron Opt. 125(15), 3868–3875 (2014)

    Article  Google Scholar 

  39. Kumari, A., Sahoo, S.K., Chinnaiah, M.C.: Fast and efficient visibility restoration technique for single image dehazing and defogging. IEEE Access 9, 48131–48146 (2021)

    Article  Google Scholar 

  40. Gonçalves-E-Silva, K., Aloise, D., et al.: Less is more: simplified nelder-mead method for large unconstrained optimization. Yugoslav J. Oper. Res. 28(2), 153–169 (2018). https://doi.org/10.2298/YJOR180120014G

    Article  MathSciNet  MATH  Google Scholar 

  41. Kou, F., Chen, W., Wen, C., Li, Z.: Gradient domain guided image filtering. IEEE Trans. Image Process. 24(11), 4528–4539 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  42. Drago, F., Myszkowski, K., Annen, T., et al.: Adaptive logarithmic mapping for displaying high contrast scenes. Comput. Graph. Forum 22(3), 419–426 (2003)

    Article  Google Scholar 

  43. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  44. Khmag, A., Al-Haddad, S.A.R., Ramli, A.R., et al.: Single image dehazing using second-generation wavelet transforms and the mean vector L2-norm. Vis. Comput. 34, 675–688 (2018)

    Article  Google Scholar 

  45. Hautiere, N., Tarel, J.P., Aubert, D., et al.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2008)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the Research on Key Technologies and Equipment of Next Generation Marine Broadband Communication (No. 2019020090-JH2/101) and the Key Technologies of Ship Perception and Network Support in a complex environment (No. 017210332).

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Dalian Maritime University, Dalian, China

    Qing Hu, Yu Zhang, Yue Zhu, Yi Jiang & Mengen Song

Authors
  1. Qing Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengen Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Zhang.

Ethics declarations

Conflict of interest

We declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Hu, Q., Zhang, Y., Zhu, Y. et al. Single image dehazing algorithm based on sky segmentation and optimal transmission maps. Vis Comput 39, 997–1013 (2023). https://doi.org/10.1007/s00371-021-02380-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02380-3

Keywords

Search

Navigation