Skip to main content
SpringerLink

ECANet: enhanced context aggregation network for single image dehazing

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

Abstract

Image dehazing is an important problem since computer recognition requires high-quality inputs. Recently, many researches tend to build an end-to-end multiscale network to restore haze-free images. But unfortunately, existing multiscale networks tend to recover under-dehazed results due to inefficient feature extraction. To solve the problem, we propose an enhanced context aggregation network for single image dehazing named ECANet. Based on encoder–decoder structure, the ECANet improves feature representation by three feature aggregation blocks (FABs) on each scale. The FAB is a new efficient feature extraction module, which adequately extracts content features and style features due to the difference receptive field between dilated convolution and ordinary convolution. To better fuse these complementary features, we combine spatial and channel attention mechanism to each FAB. After the decoding process, we also adopt an enhancing block to further refine image details under the supervision of clear references. The experimental results show that the proposed ECANet performs better than state-of-the-art dehazing methods, which recovers clear images with discriminative texture and natural color.

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

Similar content being viewed by others

References

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

  2. Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 1, 598–605 (2000)

    Google Scholar 

  3. 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 

  4. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. IEEE Int. Conf. Comput. Vis. 1, 617–624 (2014)

    Google Scholar 

  5. Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34(1), 1–9 (2014)

    Article  Google Scholar 

  6. Zhu, Q., Mai, J., Shao, L.: Single image dehazing using colour attenuation prior. Proc. Br. Mach. Vis. Conf. (BMVC) 7(2), 23578–23584 (2014)

    Google Scholar 

  7. Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, LAS Vegas, pp. 1674–1682 (2016)

    Google Scholar 

  8. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single image dehazing via multiscale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10.

    Chapter  Google Scholar 

  9. 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 

  10. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: AOD-Net: all-in-one dehazing network. In: IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, pp. 4770–4778 (2017)

    Google Scholar 

  11. Zhang, H., Pattel, V. M.: Densely connected pyramid dehazing network. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Salt Lake City, pp. 3194–3203. (2018)

    Google Scholar 

  12. Li, L., Dong, Y., Ren, W., Pan, J., Gao, C., Sang, N., Yang, M.H.: Semi-supervised image dehazing. IEEE Trans. Image Process. 29, 2766–2779 (2019)

    Article  MATH  Google Scholar 

  13. Ren, W., Ma, L., Zhang, J., Pan, J., Gao, 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, 3253–3261 (2018)

  14. Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. Proc. AAAI Conf. Artif. Intell. 34(7), 11908–11915 (2020)

    Google Scholar 

  15. Liu, X., Suganuma, M., Sun, Z., Okatani, T.: Dual residual networks leveraging the potential of paired operations for image restoration. IEEE Conference on Computer Vision and Pattern Recognition, 7000–7009 (2019).

  16. Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., Yuan, L., Hua, G.: Gated context aggregation network for image dehazing and deraining. In: IEEE Winter Conference on Applications of Computer Vision, 1375–1383 (2019)

  17. Hang, D., Jin, S., Lei, X., Hu, Z., Yang, M.H.: Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2157–2167 (2020)

  18. Liu, X., Ma, Y., Shi, Z., Chen, J.: GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE International Conference on Computer Vision, 7314–7323 (2019)

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

  20. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolutionimage synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8798–8807 (2018)

  21. Shao, Y., Li, L., Ren, W., Gao, C., Sang, N.: Domain adaptation for image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2808–2817 (2020)

  22. Zhao, S., Zhang, L., Shen, Y., Zhou, Y.: RefineDNet: a weakly supervised refinement framework for single image dehazing. IEEE Trans. Image Process. 30(1), 3391–3404 (2021). https://doi.org/10.1109/TIP.2021.3060873

    Article  Google Scholar 

  23. Chen, Z., Wang, Y., Yang, Y., Liu, D.: ‘PSD: principled synthetic-to-real dehazing guided by physical priors. In: 2021 IEEE Conference on Computer Vision and Pattern Recognition, 7176–7185 (2021)

  24. Wang, Z., Ji, S.: Smoothed dilated convolutions for improved dense prediction. Data Min. Knowl. Disc. 35(2), 2486–2495 (2021)

    MathSciNet  MATH  Google Scholar 

  25. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition, 6230–6239 (2017)

  26. Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Image Process 3(1), 47–57 (2016)

    Google Scholar 

  27. 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 

  28. Su, Y., Cui, Z., He, C., Li, A.H., Wang, T., Cheng, K.: Prior guided conditional generative adversarial network for single image dehazing. Neurocomputing 423, 620–638 (2021)

    Article  Google Scholar 

  29. 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 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  30. Kingma, D., Ba, J.: Adam, a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations, (2015)

Download references

Acknowledgements

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Funding

National Natural Science Foundation of China (61,773,389).

Author information

Authors and Affiliations

  1. Xi’an Research Institute of High-Tech, Xi’an, 710025, China

    Zhigao Cui, Nian Wang, Yanzhao Su, Wei Zhang, Yunwei Lan & Aihua Li

Authors
  1. Zhigao Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanzhao Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunwei Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aihua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanzhao Su.

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

Cui, Z., Wang, N., Su, Y. et al. ECANet: enhanced context aggregation network for single image dehazing. SIViP 17, 471–479 (2023). https://doi.org/10.1007/s11760-022-02252-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02252-w

Keywords

Search

Navigation