Skip to main content
Springer Nature Link
Log in

A lightweight attention-based network for image dehazing

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

Abstract

In current convolution-based image dehazing networks, increasing the depth and width of convolutional layers is a common strategy to improve network performance. However, this approach significantly increases the complexity and computational cost of the dehazing network. To address this issue, this paper proposes a U-shaped multi-scale adaptive selection network (UMA-Net). Without introducing additional parameters and computational costs, the network leverages the influence of different scales of convolutional kernels on the receptive field. It combines standard and dilated convolutions into the feed-forward network (FFN) to propose a multi-scale adaptive (MA) dehazing module, further expanding the receptive field and focusing on important spatial and channel information within the FFN. To fully exploit the multi-scale features of the MA module, a lightweight channel attention-guided fusion (CAGF) module is proposed, which achieves the restoration of high-quality dehazed images from hazy images. Extensive experiments demonstrate the effectiveness of the proposed modules. On the Reside SOTS dataset, it achieves state-of-the-art performance with only 0.816M parameters and 8.794G FLOPs.

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
€32.70 /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 includes VAT (France)

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

Similar content being viewed by others

Data availability

All relevant datasets used in this study will be made available upon request.

References

  1. Narasimhan, S.G., Shree, K.N.: Vision and the atmosphere. Int. J. Comput. Vis. 48, 233–254 (2002). https://doi.org/10.1023/a:1016328200723

    Article  Google Scholar 

  2. Berman, D., Treibitz, T., Avidan, S.: Single image dehazing using haze-lines. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 720–734 (2018). https://doi.org/10.1109/TPAMI.2018.2882478

    Article  Google Scholar 

  3. Berman, D., Shai A. Non-local image dehazing.In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2016)

  4. Yi, W., et al.: Gated residual feature attention network for real-time Dehazing. App. Intell. 52(15), 17449–17464 (2022). https://doi.org/10.1007/s10489-022-03157-4/metrics

    Article  Google Scholar 

  5. Yi, W., et al.: Priors-assisted dehazing network with attention supervision and detail preservation. Neural Netw. (2024). https://doi.org/10.1016/j.neunet.2024.106165

    Article  Google Scholar 

  6. Chen, Z., Zewei, H., Zhe-Ming, L.: DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention. IEEE Trans. Image Process. (2024). https://doi.org/10.1109/TIP.2024.3354108

    Article  Google Scholar 

  7. Yi, W., et al.: DCNet: dual-cascade network for single image dehazing. Neural Comput. Appl. 34(19), 16771–16783 (2022). https://doi.org/10.1007/s00521-022-07319-w

    Article  Google Scholar 

  8. Yi, W., et al.: Frequency-guidance Collaborative Triple-branch Network for single image dehazing. Displays 80, 102577 (2023). https://doi.org/10.1016/j.displa.2023.102577

    Article  Google Scholar 

  9. He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2016)

  10. Huang, G., et al. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)

  11. Ronneberger, O., Philipp F., Thomas B.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III 18. Springer International Publishing, (2015) https://www.sci-hub.ee/https://doi.org/10.1007/978-3-319-24574-4_28

  12. Luo, P., et al.: LKD-Net: large kernel convolution network for single image dehazing. In: 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, (2023) https://doi.org/10.1109/ICME55011.2023.00276

  13. Ding, X., et al.: Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2022)

  14. Yi, W., et al.: SID-Net: single image dehazing network using adversarial and contrastive learning. Multimedia Tools Appl. (2024). https://doi.org/10.1007/s11042-024-18502-7

    Article  Google Scholar 

  15. Zheng, Y., et al.: Curricular contrastive regularization for physics-aware single image dehazing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2023)

  16. Yi, W., et al.: Towards Compact Single Image Dehazing via Task-related Contrastive Network. Expert Syst. Appl. 235, 121130 (2024). https://doi.org/10.1016/j.eswa.2023.121130

    Article  Google Scholar 

  17. Song, Y., et al.: Vision transformers for single image dehazing. IEEE Trans. Image Process. 32, 1927–1941 (2023). https://doi.org/10.1109/TIP.2023.3256763

    Article  Google Scholar 

  18. Ullah, H., et al.: Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing. IEEE Trans. Image Process. 30, 8968–8982 (2021). https://doi.org/10.1109/TIP.2021.3116790

    Article  Google Scholar 

  19. Tang, G., et al. Single image dehazing via lightweight multi-scale networks. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE, (2019) https://doi.org/10.1109/BigData47090.2019.9006075

  20. Zhang, J., Dacheng, T.: FAMED-Net: A fast and accurate multi-scale end-to-end dehazing network. IEEE Trans. Image Process. 29, 72–84 (2019). https://doi.org/10.1109/TIP.2019.2922837

    Article  MathSciNet  Google Scholar 

  21. 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). https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  22. Tarel, J.-P., Nicolas H.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th international conference on computer vision. IEEE, (2009). https://www.sci-hub.ee/https://doi.org/10.1109/ICCV.2009.5459251

  23. Qin, X., et al.: FFA-Net: Feature fusion attention network for single image dehazing. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 07. (2020). https://doi.org/10.1609/aaai.v34i07.6865

  24. Haouassi, S., Di, W.: An efficient attentional image dehazing deep network using two color space (ADMC2-net). Sensors 24(2), 687 (2024). https://doi.org/10.3390/s24020687

    Article  Google Scholar 

  25. Liu, X., et al.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF international conference on computer vision. (2019)

  26. Zhang, X., et al.: Hierarchical feature fusion with mixed convolution attention for single image dehazing. IEEE Trans. Circuits Syst. Video Technol. 32(2), 510–522 (2021). https://doi.org/10.1109/TCSVT.2021.3067062

    Article  Google Scholar 

  27. Wu, H., et al.: Contrastive learning for compact single image dehazing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2021)

  28. Kolekar, M.H., Samprit B., Abhishek P.: SARain-GAN: Spatial Attention Residual UNet Based Conditional Generative Adversarial Network for Rain Streak Removal. IEEE Access (2024). https://www.sci-hub.ee/https://doi.org/10.1109/ACCESS.2024.3375909

  29. Zhang, Y., Shen Z., Huafeng L.: Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing. arXiv preprint arXiv:2403.01105 (2024). https://doi.org/10.48550/arXiv.2403.01105

  30. Xiao, B., et al.: Single uhd image dehazing via interpretable pyramid network. Signal Process. 214, 109225 (2024). https://doi.org/10.1016/j.sigpro.2023.109225

    Article  Google Scholar 

  31. Chen, J., Guanghui, Z.: Contrastive multiscale transformer for image dehazing. Sensors 24(7), 2041 (2024). https://doi.org/10.3390/s24072041

    Article  Google Scholar 

  32. Yang, L., et al.: Hierarchical semantic-guided contextual structure-aware network for satellite image dehazing. (2024). https://www.sci-hub.ee/https://doi.org/10.20944/preprints202401.0679.v1

  33. Zhang, Z., Sinan, W., Shixian, W.: Single-image snow removal algorithm based on generative adversarial networks. IET Image Proc. 17(12), 3580–3588 (2023). https://doi.org/10.1049/ipr2.12887

    Article  Google Scholar 

  34. Wang, N., et al.: Multiscale supervision-guided context aggregation network for single image dehazing. IEEE Signal Process. Lett. 29, 70–74 (2021). https://doi.org/10.1109/LSP.2021.3125272

    Article  Google Scholar 

  35. Cui, Z., et al.: ECANet: enhanced context aggregation network for single image dehazing. Signal, Image Video Process. 17(2), 471–479 (2023). https://doi.org/10.1007/s11760-022-02252-w

    Article  Google Scholar 

  36. Su, Y.Z., et al.: Physical model and image translation fused network for single-image dehazing. Pattern Recog. 142, 109700 (2023). https://doi.org/10.1016/j.patcog.2023.109700

    Article  Google Scholar 

  37. Wang, N., et al.: Prior-guided multiscale network for single-image dehazing. IET Image Process. 15(13), 3368–3379 (2021). https://doi.org/10.1049/ipr2.12333

    Article  Google Scholar 

  38. Wang, N., et al.: RGNAM: recurrent grid network with an attention mechanism for single-image dehazing. J. Electron. Imaging 30(3), 033026–033026 (2021). https://doi.org/10.1117/1.JEI.30.3.0330265

    Article  Google Scholar 

  39. Zhang, H., Vishal M.P.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2018)

  40. Xie, L., et al.: DHD-Net: A novel deep-learning-based dehazing network. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, (2020) https://doi.org/10.1109/IJCNN48605.2020.9207316

  41. Chen, W.-T., et al.: PMHLD: patch map-based hybrid learning DehazeNet for single image haze removal. IEEE Trans. Image Process. 29, 6773–6788 (2020). https://doi.org/10.1109/TIP.2020.2993407

    Article  MathSciNet  Google Scholar 

  42. Jin, Z., et al.: Reliable image dehazing by NeRF.". Opt. Express 32(3), 3528 (2024)

    Article  MathSciNet  Google Scholar 

  43. Qu, Y., et al.: Enhanced pix2pix dehazing network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2019)

  44. Zhao, D., et al.: Pyramid global context network for image dehazing. IEEE Trans. Circuits Syst. Video Technol. 31(8), 3037–3050 (2020). https://doi.org/10.1109/TCSVT.2020.3036992

    Article  Google Scholar 

  45. Wang, Z., et al.: DFR-Net: density feature refinement network for image dehazing utilizing haze density difference. IEEE Trans. Multimedia (2024). https://doi.org/10.1109/TMM.2024.3369979

    Article  Google Scholar 

  46. Wang, Y., et al.: Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning. IEEE Trans. Image Process. (2024). https://doi.org/10.1109/TIP.2024.3362153

    Article  Google Scholar 

  47. Hong, M., et al.: Distilling image dehazing with heterogeneous task imitation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2020)

  48. Fang, Z., et al.: A guiding teaching and dual adversarial learning framework for a single image dehazing. The Visual Comput. 38(11), 3563–3575 (2022). https://doi.org/10.1007/s00371-021-02184-5

    Article  Google Scholar 

  49. Zheng, Z., Chen W.: U-shaped Vision Mamba for Single Image Dehazing. arXiv preprint arXiv:2402.04139 (2024). https://doi.org/10.48550/arXiv.2402.04139

  50. Wang, C., et al.: Selfpromer: Self-prompt dehazing transformers with depth-consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 6. (2024). https://www.sci-hub.ee/https://doi.org/10.1609/aaai.v38i6.28340

  51. Siddique, N., et al.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031–82057 (2021). https://doi.org/10.1109/ACCESS.2021.3086020

    Article  Google Scholar 

  52. Li, Y., et al. Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. (2023)

  53. Li, X., et al. Selective kernel networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2019)

  54. Huynh-Thu, Q., Mohammed, G.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008). https://doi.org/10.1049/el:20080522

    Article  Google Scholar 

  55. Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  56. Dong, H., et al.: Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2020)

  57. Tu, Z., et al.: Maxim: Multi-axis mlp for image processing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2022)

  58. Guo, C.-L., et al.: Image dehazing transformer with transmission-aware 3d position embedding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (2022)

  59. Hong, M., et al.: Uncertainty-driven dehazing network. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 1. (2022). https://doi.org/10.1609/aaai.v36i1.19973

  60. Bai, H., et al.: Self-guided image dehazing using progressive feature fusion. IEEE Trans. Image Process. 31, 1217–1229 (2022). https://doi.org/10.1109/TIP.2022.3140609

    Article  Google Scholar 

  61. Li, B., et al.: Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision. (2017)

  62. Ye, T., et al.: Perceiving and modeling density for image dehazing. In: European conference on computer vision. Cham: Springer Nature Switzerland, (2022). https://doi.org/10.1007/978-3-031-19800-7_8

Download references

Acknowledgements

This work is partially supported by the Yunnan Provincial Department of Education Science Research Fund Project, China (No. 2024Y605).

Author information

Authors and Affiliations

  1. College of Mechanics and Transportation, Southwest Forestry University, Kunming, China

    Yunsong Wei, Jiaqiang Li, Rongkun Wei & Zuxiang Lin

  2. Key Laboratory of Motor Vehicle Environment Protection and Safety in Plateau Mountainous Areas of Yunnan Province, Kunming, China

    Yunsong Wei, Jiaqiang Li, Rongkun Wei & Zuxiang Lin

Authors
  1. Yunsong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaqiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rongkun Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zuxiang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.W. wrote the main manuscript text, R.W. and J.L. revised it critically for important intellectual content, and Z.L.ensured that all parts of the paper were correct. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jiaqiang Li.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

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

Wei, Y., Li, J., Wei, R. et al. A lightweight attention-based network for image dehazing. SIViP 18, 7271–7284 (2024). https://doi.org/10.1007/s11760-024-03392-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03392-x

Keywords

Search

Navigation