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Elemental Attention Mechanism-Guided Progressive Rain Removal Algorithm

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Green, Pervasive, and Cloud Computing (GPC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14503))

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Abstract

De-rainy has become a pre-processing task for most computer vision systems. Combining recursive ideas to De-rainy models is currently popular. In this paper, the EAPRN model is proposed by introducing the elemental attention mechanism in the progressive residual network model. The elemental attention mainly consists of spatial attention and channel attention, which feature-weight the feature image in both spatial and channel dimensions and combine as elemental attention features. The introduction of elemental attention can help the model improve its fitness for the rain removal task, filter out important network layers and help the network process the rainy image. Experiments show that the EAPRN model has better visual results on different datasets and the quality of the De-rainy image is further improved.

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Notes

  1. 1.

    This work was funded by the Foundation of Inner Mongolia Science and Technology Plan Funds(RZ2300000261), Natural Science Foundation of Inner Mongolia(2023LHMS01008),Doctoral Research Initiation Fund(DC2300001281).

References

  1. Liu, S., Huang, D., Wang, Y.: Receptive field block net for accurate and fast object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 404–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_24

    Chapter  Google Scholar 

  2. Lee, K.H., Hwang, J.N., Okapal, G., et al.: Driving recorder based on-road pedestrian tracking using visual SLAM and constrained multiple-kernel. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2629–2635. IEEE (2014)

    Google Scholar 

  3. Forero, A., Calderon, F.: Vehicle and pedestrian video-tracking with classification based on deep convolutional neural networks. In: 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), pp. 1–5. IEEE (2019)

    Google Scholar 

  4. Kang, L.W., Lin, C.W., Fu, Y.H.: Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans. Image Process. 21(4), 1742–1755 (2011)

    Article  MathSciNet  Google Scholar 

  5. Yang, W., Tan, R.T., Wang, S., et al.: Single image deraining: from model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4059–4077 (2020)

    Article  Google Scholar 

  6. Luo, Y., Xu, Y., Ji, H.: Removing rain from a single image via discriminative sparse coding. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3397–3405 (2015)

    Google Scholar 

  7. Zhu, L., Fu, C.W., Lischinski, D., et al.: Joint bi-layer optimization for single-image rain streak removal. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2526–2534 (2017)

    Google Scholar 

  8. Li, Y., Tan, R.T., Guo, X., et al.: Rain streak removal using layer priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2736–2744 (2016)

    Google Scholar 

  9. Yang, W., Tan, R.T., Feng, J., et al.: Deep joint rain detection and removal from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1357–1366 (2017)

    Google Scholar 

  10. Fu, X., Huang, J., Zeng, D., et al.: Removing rain from single images via a deep detail network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3855–3863 (2017)

    Google Scholar 

  11. Li, X., Wu, J., Lin, Z., et al.: Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 254–269 (2018)

    Google Scholar 

  12. Zhang, H., Patel, V.M.: Density-aware single image Deraining using a multi-stream dense network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 695–704 (2018)

    Google Scholar 

  13. Ren, D., Zuo, W., Hu, Q., et al.: Progressive image deraining networks: a better and simpler baseline. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3937–3946 (2019)

    Google Scholar 

  14. Tan, Y., Wen, Q., Qin, J., et al.: Coupled rain streak and background estimation via separable element-wise attention. IEEE Access 8, 16627–16636 (2020)

    Article  Google Scholar 

  15. Huang, D.A., Kang, L.W., Yang, M.C., et al.: Context-aware single image rain removal. In: 2012 IEEE International Conference on Multimedia and Expo, pp. 164–169. IEEE (2012)

    Google Scholar 

  16. Kim, J.H., Lee, C., Sim, J.Y., et al.: Single-image deraining using an adaptive nonlocal means filter. In: 2013 IEEE International Conference on Image Processing, pp. 914–917. IEEE (2013)

    Google Scholar 

  17. Chen, D.Y., Chen, C.C., Kang, L.W.: Visual depth guided color image rain streaks removal using sparse coding. IEEE Trans. Circuits Syst. Video Technol. 24(8), 1430–1455 (2014)

    Article  Google Scholar 

  18. Deng, L.J., Huang, T.Z., Zhao, X.L., et al.: A directional global sparse model for single image rain removal. Appl. Math. Model. 59, 662–679 (2018)

    Article  MathSciNet  Google Scholar 

  19. Xiao, J., Wang, W., Zou, W., et al.: An image rain removal algorithm via depth of field and sparse coding. Chin. J. Comput. 42(9), 2024–2034 (2019)

    Google Scholar 

  20. Son, C.H., Zhang, X.P.: Rain detection and removal via shrinkage-based sparse coding and learned rain dictionary. J. Imaging Sci. Technol. 64(3), 30501-1–30501-17 (2020)

    Google Scholar 

  21. Eigen, D., Krishnan, D., Fergus, R.: Restoring an image taken through a window covered with dirt or rain. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 633–640 (2013)

    Google Scholar 

  22. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)

    Google Scholar 

  23. Zhang, H., Sindagi, V., Patel, V.M.: Image De-rainying using a conditional generative adversarial network. IEEE Trans. Circuits Syst. Video Technol. 30(11), 3943–3956 (2019)

    Article  Google Scholar 

  24. Yan, Z., Juan, Z., Zhijun, F.: Image rain removal algorithm based on channel attention and gated recurrent unit. Appl. Res. Comput. 38(8), 2505–2509 (2021)

    Google Scholar 

  25. Chen, X., Ma, R., Dong, Z.: Research on single image deraining algorithm based on euclidean distance with adaptive progressive residual network. In: 2021 IEEE 23rd International Conference on High Performance Computing & Communications; 7th International Conference on Data Science & Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), pp. 1791–1796. IEEE (2021)

    Google Scholar 

  26. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)

    Google Scholar 

  27. Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Inner Mongolia University of Technology, Hohhot, China

    Xingzhi Chen, Ruiqiang Ma, Shanjun Zhang & Xiaokang Zhou

  2. CISDI Information Technology (Chongqing) Co., Ltd., Chongqing, China

    Xingzhi Chen, Shanjun Zhang & Xiaokang Zhou

  3. Meiji University, Tokyo, Japan

    Ruiqiang Ma, Shanjun Zhang & Xiaokang Zhou

Authors
  1. Xingzhi Chen
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  2. Ruiqiang Ma
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  3. Shanjun Zhang
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  4. Xiaokang Zhou
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Corresponding author

Correspondence to Ruiqiang Ma .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Hai Jin

  2. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  3. Huazhong University of Science and Technology, Wuhan, China

    Chen Yu

  4. Shiga University, Shiga, Japan

    Xiaokang Zhou

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, X., Ma, R., Zhang, S., Zhou, X. (2024). Elemental Attention Mechanism-Guided Progressive Rain Removal Algorithm. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_19

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  • DOI: https://doi.org/10.1007/978-981-99-9893-7_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9892-0

  • Online ISBN: 978-981-99-9893-7

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