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Multi-Scale Topology of Residual Network for Haze Removal

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Intelligent Robotics and Applications (ICIRA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15202))

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Abstract

After all these years of rapid development, as the economic level of our country has risen, new challenges have arisen for productive work in a polluted environment. Due to hazy weather, the quality of images captured by traffic monitoring devices could be degraded, affecting the accuracy of judging vehicle dynamics. In this background, we introduce MSTBNet, a fresh network design utilizing multi-scale topological residual blocks (MSTBs) to convey information across both depth and width dimensions while effectively identifying network nodes to enhance learning. The network can capture both local and global features through the transfer of information between neighbouring and distant nodes and the feedback mechanism of the network implementation. Also, due to the lightweight of the network model, it is well suited for real-time operation of target detection. Experimental results show that our proposed algorithm has superior dehazing performance compared to existing methods, which improves the accuracy of target detection and identification.

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References

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

    Google Scholar 

  2. Berman, D., Treibitz, T., Avidan, S.: Air-light estimation using haze-lines. In: 2017 IEEE International Conference on Computational Photography (ICCP), pp. 1–9 (2017)

    Google Scholar 

  3. Berman, D., Treibitz, T., Avidan, S.: Single image dehazing using haze-lines. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 720–734 (2020)

    Article  MATH  Google Scholar 

  4. 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, 5187–5198 (2016)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  6. Chen, Y., Li, Y., Wang, Y., Mi, Z., Wang, Y., Fu, X.: Robust polarization-based underwater image enhancement method using anchor brightness adaptation. Opt. Lasers Eng. 169, 107737 (2023)

    Article  Google Scholar 

  7. Chen, Z., Wang, Y., Yang, Y., Liu, D.: 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 (2021)

    Google Scholar 

  8. Deng, J., Zhu, J., Li, H., Zhang, X., Guo, F., Hou, X.: Real-time underwater polarization imaging without relying on background. Opt. Lasers Eng. 169, 107721 (2023)

    Article  Google Scholar 

  9. Deng, Q., Huang, Z., Tsai, C.-C., Lin, C.-W.: HardGAN: a haze-aware representation distillation GAN for single image dehazing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 722–738. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_43

    Chapter  Google Scholar 

  10. Ding, X., Wang, Y., Fu, X.: Multi-polarization fusion generative adversarial networks for clear underwater imaging. Opt. Lasers Eng. 152, 106971 (2022)

    Article  Google Scholar 

  11. Dong, H., et al.: Multi-scale boosted dehazing network with dense feature fusion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2154–2164 (2020)

    Google Scholar 

  12. Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34(1), 1–14 (2015)

    Article  MATH  Google Scholar 

  13. Fu, X., Liang, Z., Ding, X., Yu, X., Wang, Y.: Image descattering and absorption compensation in underwater polarimetric imaging. Opt. Lasers Eng. 132, 106115 (2020)

    Article  Google Scholar 

  14. Gao, C., et al.: Underwater polarization de-scattering imaging based on orthogonal polarization decomposition with low-pass filtering. Opt. Lasers Eng. 170, 107796 (2023)

    Article  MATH  Google Scholar 

  15. Guo, T., Li, X., Cherukuri, V., Monga, V.: Dense scene information estimation network for dehazing. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2122–2130 (2019)

    Google Scholar 

  16. Han, P., Liu, F., Wei, Y., Shao, X.: Optical correlation assists to enhance underwater polarization imaging performance. Opt. Lasers Eng. 134, 106256 (2020)

    Article  Google Scholar 

  17. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1956–1963 (2009)

    Google Scholar 

  18. Hu, H., Zhang, Y., Li, X., Lin, Y., Cheng, Z., Liu, T.: Polarimetric underwater image recovery via deep learning. Opt. Lasers Eng. 133, 106152 (2020)

    Article  MATH  Google Scholar 

  19. Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics yolov8 (2023). https://github.com/ultralytics/ultralytics

  20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  21. Lee, B.U., Lee, K., Oh, J., Kweon, I.S.: CNN-based simultaneous dehazing and depth estimation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9722–9728 (2020)

    Google Scholar 

  22. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: AOD-Net: all-in-one dehazing network. In: IEEE International Conference on Computer Vision, pp. 4780–4788 (2017)

    Google Scholar 

  23. Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28, 492–505 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  24. Li, H., Gao, J., Zhang, Y., Xie, M., Yu, Z.: Haze transfer and feature aggregation network for real-world single image dehazing. Knowl.-Based Syst. 251, 109309 (2022)

    Article  MATH  Google Scholar 

  25. Li, Y., Ruan, R., Mi, Z., Shen, X., Gao, T., Fu, X.: An underwater image restoration based on global polarization effects of underwater scene. Opt. Lasers Eng. 165, 107550 (2023)

    Article  Google Scholar 

  26. Liu, T., et al.: Polarimetric underwater image recovery for color image with crosstalk compensation. Opt. Lasers Eng. 124, 105833 (2020)

    Article  MATH  Google Scholar 

  27. Liu, Y., et al.: From synthetic to real: image dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM International Conference on Multimedia (2021)

    Google Scholar 

  28. Lu, Y., Ren, W., Su, Y., Zhang, Z., Zhang, J., Tian, J.: Polarization image demosaicking based on homogeneity space. Opt. Lasers Eng. 178, 108179 (2024)

    Article  MATH  Google Scholar 

  29. Mishra, A.K., Kumar, M., Choudhry, M.S.: Fusion of multiscale gradient domain enhancement and gamma correction for underwater image/video enhancement and restoration. Opt. Lasers Eng. 178, 108154 (2024)

    Article  MATH  Google Scholar 

  30. Narasimhan, S., Nayar, S.: Chromatic framework for vision in bad weather. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), vol. 1, pp. 598–605 (2000)

    Google Scholar 

  31. Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vision 48(3), 233–254 (2002)

    Article  MATH  Google Scholar 

  32. Pang, Y., Nie, J., Xie, J., Han, J., Li, X.: BidNet: binocular image dehazing without explicit disparity estimation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5930–5939 (2020)

    Google Scholar 

  33. Qi, P., et al.: U2R-pGAN: unpaired underwater-image recovery with polarimetric generative adversarial network. Opt. Lasers Eng. 157, 107112 (2022)

    Article  MATH  Google Scholar 

  34. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale 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 

  35. Song, Y., He, Z., Qian, H., Du, X.: Vision transformers for single image dehazing. IEEE Trans. Image Process. 32, 1927–1941 (2023)

    Article  MATH  Google Scholar 

  36. Su, Y., Lu, Y., Liu, J., Zhang, Z., Tian, J.: Polarization image demosaicking with directional cubic residual interpolation. In: 2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 657–661 (2023)

    Google Scholar 

  37. Su, Y., Zhang, Z., Lu, Y., Liu, S., Tang, Y., Tian, J.: Underwater descattering with polarization line constraint. Opt. Lasers Eng. 179, 108265 (2024)

    Article  Google Scholar 

  38. Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: 2014 IEEE International Conference on Computational Photography (ICCP), pp. 1–11 (2014)

    Google Scholar 

  39. Wang, J., et al.: Periodic integration-based polarization differential imaging for underwater image restoration. Opt. Lasers Eng. 149, 106785 (2022)

    Article  Google Scholar 

  40. Wang, X., et al.: Compensation atmospheric scattering model and two-branch network for single image dehazing. IEEE Trans. Emerg. Top. Comput. Intell. 8, 1–17 (2024)

    Article  MATH  Google Scholar 

  41. Wu, H., et al.: Contrastive learning for compact single image dehazing. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10546–10555 (2021)

    Google Scholar 

  42. Wu, H., et al.: Real-time generation of underwater de-backscattering videos using the arrayed orthogonal polarization light source and active polarization imaging. Opt. Lasers Eng. 174, 107963 (2024)

    Article  MATH  Google Scholar 

  43. Yang, K., et al.: High-quality 3D shape recovery from scattering scenario via deep polarization neural networks. Opt. Lasers Eng. 173, 107934 (2024)

    Article  Google Scholar 

  44. Yang, X., Xu, Z., Luo, J.: Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, April 2018

    Google Scholar 

  45. Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., Tao, D.: Self-augmented unpaired image dehazing via density and depth decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2027–2036 (2022)

    Google Scholar 

  46. Zhao, Y., He, W., Ren, H., Li, Y., Fu, Y.: Polarization descattering imaging through turbid water without prior knowledge. Opt. Lasers Eng. 148, 106777 (2022)

    Article  Google Scholar 

  47. Zheng, Z., et al.: Ultra-high-definition image dehazing via multi-guided bilateral learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 16180–16189 (2021)

    Google Scholar 

  48. 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  MATH  Google Scholar 

  49. Zhu, Z., Qiu, H., Hu, Q., Ren, K., Zhou, L., Zhu, T.: Underwater 3d reconstruction based on double N-step orthogonal polarization state phase shift strategy. Opt. Lasers Eng. 178, 108161 (2024)

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant U2013210 and Grant 62105372; in part by the LiaoNing Science Fund for Distinguished Young Scholars under Grant 2023JH6/100500005.

Author information

Authors and Affiliations

  1. School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China

    Yusheng Ge & Sen Lin

  2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Yusheng Ge, Yang Lu, Yiming Su, Zhenshuo Yang & Jiandong Tian

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yang Lu, Yiming Su & Zhenshuo Yang

Authors
  1. Yusheng Ge
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  2. Yang Lu
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  3. Sen Lin
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  4. Yiming Su
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  5. Zhenshuo Yang
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  6. Jiandong Tian
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Corresponding author

Correspondence to Sen Lin .

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Editors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, China

    Xuguang Lan

  2. Xi’an Jiaotong University, Xi’an, China

    Xuesong Mei

  3. Xi’an Jiaotong University, Xi’an, China

    Caigui Jiang

  4. Xi’an Jiaotong University, Xi’an, China

    Fei Zhao

  5. Xi'an Jiaotong University, Xi'an, China

    Zhiqiang Tian

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Ge, Y., Lu, Y., Lin, S., Su, Y., Yang, Z., Tian, J. (2025). Multi-Scale Topology of Residual Network for Haze Removal. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15202. Springer, Singapore. https://doi.org/10.1007/978-981-96-0774-7_5

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  • DOI: https://doi.org/10.1007/978-981-96-0774-7_5

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