Skip to main content

Advertisement

Springer Nature Link
Log in

Unsupervised single-image dehazing via self-guided inverse-retinex GAN

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

With the fast growth of deep learning, trainable frameworks have been presented to restore hazy images. However, the capability of most existing learning-based methods is limited since the parameters learned in an end-to-end manner are difficult to generalize to the haze or foggy images captured in the real world. Another challenge of extending data-driven models into image dehazing is collecting a large number of hazy and haze-free image pairs for the same scenes, which is impractical. To address these issues, we explore unsupervised single-image dehazing and propose a self-guided generative adversarial network (GAN) based on the dual relationship between dehazing and Retinex. Specifically, we carry out image dehazing as illumination-reflectance separation using a decomposition net in the generator. Then, a guide module is applied to encourage local structure preservation and realistic reflectance generation. In addition, we integrate the model with the outdoor heavy-duty pan-tilt-zoom (PTZ) camera to implement dynamic object detection in hazy environment. We comprehensively evaluate the proposed GAN with both synthetic and real-world scenes. The quantitative and qualitative results demonstrate the effectiveness and robustness of our model in handling unseen hazy images with varying visual properties.

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
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available from the following public domain resources: https://github.com/xiaofeng94/RefineDNet-for-dehazing.

References

  1. Yang, Y., Hou, C., Huang, H., Zhang, Z., Xie, G.: Cascaded deep residual learning network for single image dehazing. Multimed. Syst. 29(4), 2037–2048 (2023)

    Article  Google Scholar 

  2. Khan, H., Xiao, B., Li, W., Muhammad, N.: Recent advancement in haze removal approaches. Multimed. Syst. 28(3), 687–710 (2022)

    Article  Google Scholar 

  3. Ren, W., Zhou, L., Chen, J.: Unsupervised single image dehazing with generative adversarial network. Multimed. Syst. 29(5), 2923–2933 (2023)

    Article  Google Scholar 

  4. Liu, R.W., Guo, Y., Lu, Y., Chui, K.T., Gupta, B.B.: Deep network-enabled haze visibility enhancement for visual iot-driven intelligent transportation systems. IEEE Trans. Industr. Inf. 19(2), 1581–1591 (2022)

    Article  Google Scholar 

  5. Jia, T., Li, J., Zhuo, L., Li, G.: Effective meta-attention dehazing networks for vision-based outdoor industrial systems. IEEE Trans. Industr. Inf. 18(3), 1511–1520 (2021)

    Article  Google Scholar 

  6. 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  Google Scholar 

  7. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)

  8. Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3194–3203 (2018)

  9. Qu, Y., Chen, Y., Huang, J., Xie, Y.: Enhanced pix2pix dehazing network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8160–8168 (2019)

  10. Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., Yang, M.-H.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)

  11. Liu, X., Ma, Y., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7314–7323 (2019)

  12. Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. IEEE Trans. Image Process. 29, 2692–2701 (2019)

    Article  Google Scholar 

  13. Li, B., Gou, Y., Gu, S., Liu, J.Z., Zhou, J.T., Peng, X.: You only look yourself: Unsupervised and untrained single image dehazing neural network. Int. J. Comput. Vision 129, 1754–1767 (2021)

    Article  Google Scholar 

  14. Li, B., Gou, Y., Liu, J.Z., Zhu, H., Zhou, J.T., Peng, X.: Zero-shot image dehazing. IEEE Trans. Image Process. 29, 8457–8466 (2020)

    Article  Google Scholar 

  15. 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 

  16. Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., Tao, D.: Self-augmented unpaired image dehazing via density and depth decomposition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2037–2046 (2022)

  17. Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J., Bertalmío, M.: On the duality between retinex and image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8212–8221 (2018)

  18. Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008). IEEE

  19. Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)

    Article  MathSciNet  Google Scholar 

  20. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)

    Google Scholar 

  21. Berman, D., Avidan, S., etal.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)

  22. Kim, S.E., Park, T.H., Eom, I.K.: Fast single image dehazing using saturation based transmission map estimation. IEEE Trans. Image Process. 29, 1985–1998 (2019)

    Article  MathSciNet  Google Scholar 

  23. Ju, M., Ding, C., Ren, W., Yang, Y., Zhang, D., Guo, Y.J.: Ide: Image dehazing and exposure using an enhanced atmospheric scattering model. IEEE Trans. Image Process. 30, 2180–2192 (2021)

    Article  Google Scholar 

  24. Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)

    Article  Google Scholar 

  25. Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  26. Rahman, Z.-U., Jobson, D.J., Woodell, G.A.: Retinex processing for automatic image enhancement. J. Electron. Imaging 13(1), 100–110 (2004)

    Article  Google Scholar 

  27. Li, P., Tian, J., Tang, Y., Wang, G., Wu, C.: Deep retinex network for single image dehazing. IEEE Trans. Image Process. 30, 1100–1115 (2020)

    Article  Google Scholar 

  28. Li, J., Zhuo, L., Zhang, H., Li, G., Xiong, N.: Effective data-driven technology for efficient vision-based outdoor industrial systems. IEEE Trans. Industr. Inf. 16(7), 4344–4354 (2019)

    Article  Google Scholar 

  29. Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)

    Article  MathSciNet  Google Scholar 

  30. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

  31. Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16, pp. 319–345 (2020). Springer

  32. Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2018)

    Article  MathSciNet  Google Scholar 

  33. Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015)

    Article  MathSciNet  Google Scholar 

  34. Ling, P., Chen, H., Tan, X., Jin, Y., Chen, E.: Single image dehazing using saturation line prior. IEEE Trans. Image Process. 32, 3238–3253 (2023)

    Article  Google Scholar 

  35. Li, X., Yu, H., Zhao, C., Fan, C., Zou, L.: Dadrnet: Cross-domain image dehazing via domain adaptation and disentangled representation. Neurocomputing 544, 126242 (2023)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.62002039, No.61672122, No.61802045), and the Fundamental Research Funds for the Central Universities (No.36330603).

Author information

Authors and Affiliations

  1. School of Space Information, Space Engineering University, Beijing, 101400, China

    Hui Chen

  2. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Rong Chen, Haoran Li & Nannan Li

  3. Department of Intelligent Science, Xi’an Jiaotong-Liverpool University, Suzhou, 215000, Jiangsu, China

    Yushi Li

Authors
  1. Hui Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yushi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Haoran Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Nannan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Hui Chen: Conceptualization and Writing; Rong Chen and Yushi Li: Methodology; Haoran Li and Nannan Li: Validation.

Corresponding authors

Correspondence to Rong Chen or Yushi Li.

Ethics declarations

Conflict of interest

The authors declare that they have no Conflict of interest or Conflict of interest.

Ethical approval

This submission does not include human or animal research.

Additional information

Communicated by Hongtao Xie.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Chen, R., Li, Y. et al. Unsupervised single-image dehazing via self-guided inverse-retinex GAN. Multimedia Systems 31, 139 (2025). https://doi.org/10.1007/s00530-025-01713-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00530-025-01713-9

Keywords