Skip to main content
SpringerLink
Log in

Progressive image dehazing network based on dual feature extraction modules

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Image dehazing is of great importance and has been widely studied, as haze severely affects many high-level computer vision tasks. In this paper, by considering the gradual dissipation process of haze, a progressive dehazing network (PDN) is proposed. The proposed approach realizes haze removal step by step by constructing two main modules: the preliminary and fine dehazing modules. In the preliminary dehazing module, a combined residual block is first constructed to extract and enhance features of different levels. Then, an adaptive feature fusion strategy is designed to integrate these features and output the initial dehazing result. Aiming at the residual haze in the initial results, a fine dehazing module is constructed by simulating the last period of the haze dissipation process to further extract a fine haze layer. The final dehazing result is obtained by removing the fine haze layer from the initial dehazing result. Experimental results indicate that the proposed method is superior to some state-of-the-art dehazing methods in terms of visual comparison and objective evaluation.

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
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Dudhane A, Biradar K, Patil P, Hambarde P, Murala S (2020) Varicolored image de-hazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 13–19

  2. Yao L, Xu H, Zhang W, Liang X, Li Z (2020) SM-NAS: structural-to-modular neural architecture search for object detection. In: AAAI Conf Artif Intell, pp 12661–12668

  3. Xu Y, Fu M, Wang Q, Wang Y, Chen K, Xia G, Bai X (2021) Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans Pattern Anal Mach Intell 43(4):1452–1459

    Article  Google Scholar 

  4. Deng W, Zheng L, Ye Q, Kang G, Yi Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proc. IEEE Conf Comput Vis Pattern Recognit, pp 994–100

  5. Al-Sammaraie M (2015) Contrast enhancement of roads images with foggy scenes based on histogram equalization. In: Proc. IEEE ICCSE, pp 95–101

  6. Yang W, Wang R, Fang S, Zhang X (2010) Variable filter Retinex algorithm for foggy image enhancement. J Comput Aided Des Comput Graph 22(6):965–971

    Google Scholar 

  7. Jia J, Yue H (2014) A wavelet-based approach to improve foggy image clarity. In: Proc. IFAC, pp 930–935

  8. Cantor A (1978) Optics of the atmosphere: scattering by molecules and particles. IEEE J Quantum Electron 14(9):698–699

    Article  Google Scholar 

  9. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1674–1682

  10. Fattal R (2014) Dehazing using color-lines. ACM Trans Graph 34(1):1–14

    Article  Google Scholar 

  11. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  12. Tufail Z, Khurshid K, Salman A, Nizami I, Khurshid K, Jeon B (2018) Improved dark channel prior for image defogging using RGB and YCbCr color space. IEEE Access 6(1):32576–32587

    Article  Google Scholar 

  13. Chen W, Ding J, Kuo S (2019) PMS-Net: robust haze removal based on patch map for single images. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 11673–116821

  14. Chen D, He M, Fan Q, Liao J, Zhang L, Hou D, Yuan L, Hua G (2019) Gated context aggregation network for image dehazing and deraining. In: Proc IEEE Winter Conf Appl Comput Vis (WACV), pp 1375–1383

  15. Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced Pix2pix dehazing network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 8160–8168

  16. Pang Y, Nie J, Xie J, Han J, Li X (2020) BidNet: binocular image dehazing without explicit disparity estimation. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 5930–5939

  17. Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2805–2814

  18. Li R, Pan J, He M, Li Z, Tang J (2020) Task-Oriented Network for Image Dehazing. IEEE Trans Image Process 29:6523–6534

    Article  MATH  Google Scholar 

  19. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M (2016) Single image dehazing via multi-scale convolutional neural networks. In: Proc European Conf Comput Vis (ECCV), pp 154–169

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

    Article  MathSciNet  MATH  Google Scholar 

  21. Li B, Peng X, Wang Z, Xu J, Feng D (2017) AOD-Net: all-in-one dehazing network. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 4780–4788

  22. Zhang H, Patel V (2018) Densely connected pyramid dehazing network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 3194–3203

  23. Guo T, Monga V (2020) Reinforced depth-aware deep learning for single image dehazing. In: Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP), pp 8891–8895

  24. Lee B, Lee K, Oh J, Kweon I (2020) CNN-based simultaneous dehazing and depth estimation. In: Proc IEEE Int Conf Robot Autom (ICRA), pp 9722–9728

  25. Wang C, Huang Y, Zou Y, Xu Y (2021) FWB-NET:front white balance network for color shift correction in single image dehazing via atmospheric light estimation. In: Proc. IEEE Int Conf Acoust Speech Signal Process (ICASSP), pp 2040–2044

  26. Ullah H, Muhammad K, Irfan M, Anwar S, Sajjad M, Imran A, Albuquerque V (2021) Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing. IEEE Trans Image Process 30:8968–8982

    Article  Google Scholar 

  27. VaswaniA, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: NeurIPS, pp 1–11

  28. Song Y, He Z, Qian H, Du X (2022) Vision transformers for single image dehazing. arXiv preprint arXiv:2204.03883

  29. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 10012–10022

  30. Li K, Yu R, Wang Z, Yuan L, Song G, Chen J (2022) Locality guidance for improving vision transformers on tiny datasets. arXiv preprint arXiv:2207.10026

  31. Yang Y, Jiao L, Liu X, Fang L, Yang S, Feng Z, Tang X (2022) Transformers meet visual learning understanding: a comprehensive review. arXiv preprint arXiv:2203.12944

  32. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(11):600–612

    Article  Google Scholar 

  33. Wang Z, Simoncelli E, Bovik A (2003) Multiscale structural similarity for image quality assessment. In: Proc. 37th Asilomar Conf Signals Syst Comput, pp 1398–1402

  34. Sharma G, Wu W, Dalal E (2005) The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30

    Article  Google Scholar 

  35. Liu X, Ma Y, Shi Z, Chen J (2019) GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 7313–7322

  36. Hong M, Xie Y, Li C, Qu Y (2020) Distilling image dehazing with heterogeneous task imitation. In: Proc. IEEE Conf Comput Vis Pattern Recognit, pp 3459–3468

  37. Liu Z, Xiao B, Alrabeiah M, Wang K, Chen J (2019) Single image dehazing with a generic model agnostic convolutional neural network. IEEE Signal Process Lett 26(6):833–837

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhang Y, Ding L, Sharma G (2017) HazeRD: an outdoor scene dataset and benchmark for single image dehazing. In: Proc IEEE Int Conf Image Process (ICIP), pp 3205–3209

  40. Zhu X, Lyu S, Wang X et al (2021) TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 2778–2788

  41. Lin T, Maire M, Belongie S et al (2014) Microsoft COCO: common objects in context. In: Proc European Conf Comput Vis (ECCV), pp 740–755

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61862030, No. 62072218, and 62261025), by the Natural Science Foundation of Jiangxi Province (No. 20182BCB22006, No. 20181BAB202010, No. 20192ACB20002, and No. 20192ACBL21008), and by the Talent project of Jiangxi Thousand Talents Program (No. jxsq2019201056).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Yong Yang

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Wei Hu & Juwei Guan

  3. School of Software, Tiangong University, Tianjin, 300387, China

    Shuying Huang

  4. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Weiguo Wan

Authors
  1. Yong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiguo Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juwei Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuying Huang.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 kb)

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

Yang, Y., Hu, W., Huang, S. et al. Progressive image dehazing network based on dual feature extraction modules. Int. J. Mach. Learn. & Cyber. 14, 2169–2180 (2023). https://doi.org/10.1007/s13042-022-01753-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-022-01753-x

Keywords

Search

Navigation