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HDUD-Net: heterogeneous decoupling unsupervised dehaze network

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Abstract

Haze reduces the imaging effectiveness of outdoor vision systems, significantly degrading the quality of images; hence, reducing haze has been a focus of many studies. In recent years, decoupled representation learning has been applied in image processing; however, existing decoupled networks lack a specific design for information with different characteristics to achieve satisfactory results in dehazing tasks. This study proposes a heterogeneous decoupling unsupervised dehazing network (HDUD-Net). Heterogeneous modules are used to learn the content and haze information of images individually to separate them effectively. To address the problem of information loss when extracting the content from hazy images with complex noise, this study proposes a bi-branch multi-hierarchical feature fusion module. Additionally, it proposes a style feature contrast learning method to generate positive and negative sample queues and construct contrast loss for enhancing decoupling performance. Numerous experiments confirm that the proposed algorithm achieves higher performance according to objective metrics and a more realistic visual effect when compared with state-of-the-art single-image dehazing algorithms.

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Data availability

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

References

  1. Yang Y, Wang C, Liu R, Zhang L, Guo X, Tao D (2022) Self-augmented unpaired image dehazing via density and depth decomposition. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 2027–2036

  2. Wu Y, Tao D, Zhan Y, Zhang C (2022) Bin-flow: bidirectional normalizing flow for robust image dehazing. IEEE Trans Image Process 31:6635–6648

    Article  Google Scholar 

  3. Chen J, Yang G, Xia M, Zhang D (2022) From depth-aware haze generation to real-world haze removal. Neural Comput Appl 1–13

  4. Gao X, Tang P, Cheng Q, Li J (2022) Air infrared small target local dehazing based on multiple-factor fusion cascade network. Neural Comput Appl 1–9

  5. Sun H, Zhang Y, Chen P, Dan Z, Sun S, Wan J, Li W (2021) Scale-free heterogeneous cyclegan for defogging from a single image for autonomous driving in fog. Neural Comput Appl 1–15

  6. Mccartney EJ, Hall FF (1976) Optics of the atmosphere: scattering by molecules and particles. Phys Today 30:76–77

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Zhu Q, Mai J, Shao L (2014) Single image dehazing using color attenuation prior. In: BMVC

  10. Berman D, Treibitz T, Avidan S (2020) Single image dehazing using haze-lines. IEEE Trans Pattern Anal Mach Intell 42:720–734

    Article  Google Scholar 

  11. Bui TM, Kim W (2018) Single image dehazing using color ellipsoid prior. IEEE Trans Image Process 27:999–1009

    Article  MathSciNet  Google Scholar 

  12. Ju M, Ding C, Guo YJ, Zhang D-Y (2020) IDGCP: image dehazing based on gamma correction prior. IEEE Trans Image Process 29:3104–3118

    Article  Google Scholar 

  13. Ju M, Ding C, Guo CA, Ren W, Tao D (2021) IDRLP: image dehazing using region line prior. IEEE Trans Image Process 30:9043–9057

    Article  Google Scholar 

  14. 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 25:5187–5198

    Article  MathSciNet  Google Scholar 

  15. Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All-in-one dehazing network. In: 2017 IEEE international conference on computer vision (ICCV), pp 4780–4788

  16. Zhao S, Zhang L, Shen Y, Zhou Y (2021) Refinednet: a weakly supervised refinement framework for single image dehazing. IEEE Trans Image Process

  17. Li B, Gou Y, Liu JZ, Zhu H, Peng X (2020) Zero-shot image dehazing. IEEE Trans Image Process

  18. Li B, Gou Y, Gu S, Liu J, Zhou JT, Peng X (2021) You only look yourself: unsupervised and untrained single image dehazing neural network. Int J Comput Vis 129:1754–1767

    Article  Google Scholar 

  19. Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 2805–2814

  20. Li L, Dong Y, Ren W, Pan J, Gao C, Sang N, Yang M-H (2020) Semi-supervised image dehazing. IEEE Trans Image Process 29:2766–2779

    Article  Google Scholar 

  21. Chen Z, Wang Y, Yang Y, Liu D (2021) PSD: principled synthetic-to-real dehazing guided by physical priors. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 7176–7185

  22. Liu Y, Zhu L, Pei S, Fu H, Qin J, Zhang Q, Wan L, Feng W (2021) From synthetic to real: Image dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM international conference on multimedia

  23. Agrawal SC, Jalal AS (2022) Dense haze removal by nonlinear transformation. IEEE Trans Circuits Syst Video Technol 32:593–607

    Article  Google Scholar 

  24. Ju M, Ding C, Ren W, Yang Y (2022) IDBP: image dehazing using blended priors including non-local, local, and global priors. IEEE Trans Circuits Syst Video Technol 32:4867–4871

    Article  Google Scholar 

  25. Hong M, Xie Y, Li C, Qu Y (2020) Distilling image dehazing with heterogeneous task imitation. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 3459–3468

  26. Liu X, Zhang T, Zhang J (2022) Toward visual quality enhancement of dehazing effect with improved cycle-gan. Neural Comput Appl 1–14

  27. Engin D, Genç A, Ekenel HK (2018) Cycle-dehaze: Enhanced cyclegan for single image dehazing. In: 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 938–9388

  28. Liu M-Y, Breuel TM, Kautz J (2017) Unsupervised image-to-image translation networks. ArXiv arXiv:1703.00848

  29. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. ArXiv arXiv:1703.07737

  30. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR)

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

    Article  MathSciNet  Google Scholar 

  32. Tarel J-P, Hautiére N (2009) Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th international conference on computer vision, pp 2201–2208

  33. Hénaff O, Srinivas A, Fauw JD, Razavi A, Doersch C, Eslami S, Oord A (2019) Data-efficient image recognition with contrastive predictive coding

  34. Dong H, Pan J, Xiang L, Hu Z, Zhang X, Wang F, Yang M-H (2020) Multi-scale boosted dehazing network with dense feature fusion. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 2154–2164

  35. Qin X, Wang Z, Bai Y, Xie X, Jia H (2020) FFA-Net: feature fusion attention network for single image dehazing. In: AAAI

  36. Zhang C, Wu, C (2022) Multi-scale attentive feature fusion network for single image dehazing. In: 2022 International joint conference on neural networks (IJCNN), pp 1–7

  37. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. ArXiv arXiv:1505.04597

  38. Lin C, Rong X, Yu X (2022) Msaff-net: multiscale attention feature fusion networks for single image dehazing and beyond. IEEE Trans Multimed

  39. Yi X, Ma B, Zhang Y, Liu L, Wu JH (2022) Two-step image dehazing with intra-domain and inter-domain adaptation. Neurocomputing 485:1–11

    Article  Google Scholar 

  40. Yi W, Dong L, Liu M, Zhao Y, Hui M, Kong L (2022) DCNet: dual-cascade network for single image dehazing. Neural Comput Appl 34(19):16771–16783

    Article  Google Scholar 

  41. Tian Y, Krishnan D, Isola P (2020) Contrastive multiview coding. In: ECCV

  42. Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M-H (2018) Gated fusion network for single image dehazing. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 3253–3261

  43. Gutmann M, Hyvrinen A (2010) Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: International conference on artificial intelligence and statistics

  44. Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 3194–3203

  45. Chen X, Pan J, Jiang K, Li Y, Huang Y, Kong C, Dai L, Fan Z-C (2021) Unpaired deep image deraining using dual contrastive learning

  46. Zhu H, Peng X, Chandrasekhar VR, Li L, Lim J-H (2018) Dehazegan: when image dehazing meets differential programming. In: IJCAI

  47. Yang X, Xu Z, Luo J (2018) Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: AAAI

  48. Ye Y, Chang Y, Zhou H, Yan L (2021) Closing the loop: joint rain generation and removal via disentangled image translation

  49. Zhang Y, Li M, Li R, Jia K, Zhang L (2022) Exact feature distribution matching for arbitrary style transfer and domain generalization. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 8025–8035

  50. Zhang Y-X, Tang F, Dong W, Huang H, Ma C, Lee T-Y, Xu C (2022) Domain enhanced arbitrary image style transfer via contrastive learning. In: ACM SIGGRAPH 2022 conference proceedings

  51. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci

  52. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2019) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28:492–505

    Article  MathSciNet  Google Scholar 

  53. Zhang Y, Ding L, Sharma G (2017) Hazerd: an outdoor scene dataset and benchmark for single image dehazing. In: 2017 IEEE international conference on image processing (ICIP), 3205–3209

  54. Yang Y, Wang C, Liu R, Zhang L, Guo X, Tao D (2022) Self-augmented unpaired image dehazing via density and depth decomposition. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 2027–2036. https://doi.org/10.1109/CVPR52688.2022.00208

  55. Ancuti CO, Ancuti C, Timofte R (2020) NH-HAZE: an image dehazing benchmark with non-homogeneous hazy and haze-free images. IEEE

  56. Ancuti CO, Ancuti C, Vasluianu FA, Timofte R, Mandal M (2020) Ntire 2020 challenge on nonhomogeneous dehazing

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grant 62371015, in part by the Beijing Natural Science Foundation under Grant L211017, in part by the General Program of Beijing Municipal Education Commission under Grant KM202110005027, and in part by National Natural Science Foundation of China under Grant 61971016 and 61701011.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Jiafeng Li, Lingyan Kuang, Jiaqi Jin, Li Zhuo & Jing Zhang

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

    Jiafeng Li, Lingyan Kuang, Jiaqi Jin, Li Zhuo & Jing Zhang

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  1. Jiafeng Li
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  2. Lingyan Kuang
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  3. Jiaqi Jin
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  4. Li Zhuo
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  5. Jing Zhang
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Correspondence to Jiafeng Li.

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Li, J., Kuang, L., Jin, J. et al. HDUD-Net: heterogeneous decoupling unsupervised dehaze network. Neural Comput & Applic 36, 2695–2711 (2024). https://doi.org/10.1007/s00521-023-09199-0

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