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Enhanced Frequency Information for Image Dehazing

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14355))

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Abstract

The restoration of images affected by severe weather conditions such as heavy fog is a trending topic in the field of computer vision. Despite the fact that many image dehazing methods have achieved impressive performance, it is common that frequency information attenuation is overlooked in both feature space and frequency domain. In this paper, we propose a novel frequency guidance (FG) framework for image dehazing, which contains a recurrent frequency enhancement (RFE) module and a reconstruction module. To begin with, we develop a multi-scale decomposition (MD) block to separate the feature map into high-frequency and low-frequency components. Subsequently, both components are enhanced using the same network guided by the attention map, but with different weights. In addition to enhancing the frequency information within the feature space, we introduce a Fourier frequency loss (FFL) to provide guidance in the frequency domain, obtained via the fast Fourier transform. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple dehazing datasets.

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References

  1. 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 

  2. Cai, M., Zhang, H., Huang, H., Geng, Q., Li, Y., Huang, G.: Frequency domain image translation: more photo-realistic, better identity-preserving. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13930–13940 (2021)

    Google Scholar 

  3. Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. In: 2019 IEEE Winter conference on Applications of Computer Vision, pp. 1375–1383. IEEE (2019)

    Google Scholar 

  4. Chen, Z., He, Z., Lu, Z.M.: DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention. arXiv preprint arXiv:2301.04805 (2023)

  5. 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, pp. 2157–2167 (2020)

    Google Scholar 

  6. Dong, J., Pan, J.: Physics-based feature dehazing networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 188–204. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_12

    Chapter  Google Scholar 

  7. Dong, Y., Liu, Y., Zhang, H., Chen, S., Qiao, Y.: FD-GAN: generative adversarial networks with fusion-discriminator for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 10729–10736 (2020)

    Google Scholar 

  8. Fan, J., Guo, F., Qian, J., Li, X., Li, J., Yang, J.: Non-aligned supervision for real image dehazing. arXiv preprint arXiv:2303.04940 (2023)

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

    Article  Google Scholar 

  10. Fu, M., Liu, H., Yu, Y., Chen, J., Wang, K.: DW-GAN: a discrete wavelet transform GAN for nonhomogeneous dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 203–212 (2021)

    Google Scholar 

  11. Guo, C.L., Yan, Q., Anwar, S., Cong, R., Ren, W., Li, C.: Image dehazing transformer with transmission-aware 3D position embedding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5812–5820 (2022)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)

    Article  Google Scholar 

  14. He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 558–567 (2019)

    Google Scholar 

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

  16. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: AOD-Net: all-in-one dehazing network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4770–4778 (2017)

    Google Scholar 

  17. Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2018)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  19. 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, pp. 50–58 (2021)

    Google Scholar 

  20. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. vol. 2, pp. 820–827. IEEE (1999)

    Google Scholar 

  21. Park, J., Han, D.K., Ko, H.: Fusion of heterogeneous adversarial networks for single image dehazing. IEEE Trans. Image Process. 29, 4721–4732 (2020)

    Article  MATH  Google Scholar 

  22. Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 11908–11915 (2020)

    Google Scholar 

  23. 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 

  24. Ren, W., et al.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)

    Google Scholar 

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

  26. Song, Y., Zhou, Y., Qian, H., Du, X.: Rethinking performance gains in image dehazing networks. arXiv preprint arXiv:2209.11448 (2022)

  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, pp. 10551–10560 (2021)

    Google Scholar 

  28. Ye, T., et al.: Perceiving and modeling density is all you need for image dehazing. arXiv preprint arXiv:2111.09733 (2021)

  29. Yuan, J., Jiang, H., Li, X., Qian, J., Li, J., Yang, J.: Recurrent structure attention guidance for depth super-resolution. arXiv preprint arXiv:2301.13419 (2023)

  30. 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 

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Author information

Authors and Affiliations

  1. PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Fei Guo, Junkai Fan, Jun Li & Jian Yang

Authors
  1. Fei Guo
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  2. Junkai Fan
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  3. Jun Li
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  4. Jian Yang
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Corresponding authors

Correspondence to Jun Li or Jian Yang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Guo, F., Fan, J., Li, J., Yang, J. (2023). Enhanced Frequency Information for Image Dehazing. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_18

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  • DOI: https://doi.org/10.1007/978-3-031-46305-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46304-4

  • Online ISBN: 978-3-031-46305-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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