Skip to main content
SpringerLink
Log in

Image shadow removal algorithm guided by progressive attention mechanism

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Current shadow removal algorithms generally require prior knowledge. When restoring dark areas or shadow areas with complex textures, there are problems of incomplete restoration of details and distortion. In this paper, a novel image shadow removal algorithm guided by progressive attention mechanism (PAGAN) was proposed for the residual and incomplete shadow removal of complex objects or dark areas. The algorithm combines an attention mechanism with feature fusion technology, which improves the feature location accuracy and enriches the feature information. First, in the feature extraction stage of the generation network, dilated convolution residual blocks were used with different learning rates for feature extraction to expand the receptive field of the network. Then, a parallel attention mechanism was used in the auto-encoder of the generation network to guide the generation network to compile the details of the shadowless image. Second, in the auto-encoding stage, a multi-layer and multi-scale feature fusion method was applied to incorporate the global semantic information and local detail features. Finally, the series attention mechanism was used to guide the discrimination network to identify the shadowless image generated by the generation network to reduce the loss of key features and enhance the identification ability of the discrimination network. Experiments based on open datasets, SRD and ISTD, indicated good visual effect. The structural similarity index measure value of the algorithm can reach 97.5 \(\%\), the peak signal-to-noise ratio can reach 32.8, and the root mean square error can be reduced to 5.8.

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

Similar content being viewed by others

References

  1. Panagopoulos, A., Wang, C., Samaras, D., Paragios, N.: Illumination estimation and cast shadow detection through a higher-order graphical model. In: CVPR vol. 2011, pp. 673–680 (2011)

  2. Tian, J., Qi, X., Liangqiong, Q., Tang, Y.: New spectrum ratio properties and features for shadow detection. Pattern Recognit. 51, 85–96 (2016)

    Article  Google Scholar 

  3. Usrika, S.A., Sattar, A.: Shadow detection from real images and removal using image processing. In: Data Engineering for Smart Systems, pp. 451–460. Springer, (2022)

  4. Nair, V., Ram, P.G.K., Sundararaman, S.: Shadow detection and removal from images using machine learning and morphological operations. J. Eng. 2019(1), 11–18 (2019)

    Article  Google Scholar 

  5. Guo, R., Dai, Q., Hoiem, D.: Paired regions for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2956–2967 (2012)

    Article  Google Scholar 

  6. Nikkil Kumar, PC., Malathi, P.: A survey on various shadow detection and removal methods. In: International Conference On Computational Vision and Bio Inspired Computing, pp. 395–401. Springer, (2020)

  7. Hu, X., Jiang, Y., Fu, C.W., Heng, P.A.: Mask-shadowgan: learning to remove shadows from unpaired data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2472–2481 (2019)

  8. Fan, H., Han, M., Li, J.: Image shadow removal using end-to-end deep convolutional neural networks. Appl. Sci. 9(5), 1009 (2019)

    Article  Google Scholar 

  9. Hu, X., Fu, C.W., Zhu, L., Qin, J., Heng, P.A.: Direction-aware spatial context features for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 42(11), 2795–2808 (2019)

    Article  Google Scholar 

  10. Gong, H., Cosker, D.: User-assisted image shadow removal. Image Vision Comput 62, 19–27 (2017)

    Article  Google Scholar 

  11. Deb, K., Suny, A.H.: Shadow detection and removal based on ycbcr color space. Smart Comput. Rev. 4(1), 23–33 (2014)

    Article  Google Scholar 

  12. Su, Y.-F., Chen, H.H.: A three-stage approach to shadow field estimation from partial boundary information. IEEE Trans. Image Process. 19(10), 2749–2760 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Liu, F., Gleicher, M.: Texture-consistent shadow removal. In: European Conference on Computer Vision, pp. 437–450. Springer (2008)

  14. Zhang, J., Zhou, Q., Jun, W., Wang, Y., Wang, H., Li, Y., Liu, Y.: A cloud detection method using convolutional neural network based on gabor transform and attention mechanism with dark channel subnet for remote sensing image. Remote Sens. 12(19), 3261 (2020)

    Article  Google Scholar 

  15. Zhang, X., Wang, T., Qi, J., Lu, H., Wang, G.: Progressive attention guided recurrent network for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 714–722 (2018)

  16. Liu, Z., Duan, Q., Shi, S., Zhao, P.: Multi-level progressive parallel attention guided salient object detection for rgb-d images. Vis. Comput. 37(3), 529–540 (2021)

    Article  Google Scholar 

  17. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)

    Article  Google Scholar 

  18. Xiong, W., Xiong, Z., Cui, Y., Lv, Y.: Deep multi-feature fusion network for remote sensing images. Remote Sens. Lett. 11(6), 563–571 (2020)

    Article  Google Scholar 

  19. Li, A., Qi, J.Q., Huchuan, L.: Multi-attention guided feature fusion network for salient object detection. Neurocomputing 411, 416–427 (2020)

  20. Javed, K., Din, U.: Nizam, Hussain, Ghulam, Farooq, Tahir: throwaway shadows using parallel encoders generative adversarial network. Appl. Sci. 12(2), 824 (2022)

    Article  Google Scholar 

  21. Cai, J., Dai, X., Hong, L., Gao, Z., Qiu, Z.: An air quality prediction model based on a noise reduction self-coding deep network. Math. Probl. Eng., 2020, (2020)

  22. Zhenyu, L., Bai, Y., Chen, Y., Chunqiu, S., Shuihua, L.: The classification of gliomas based on a pyramid dilated convolution resnet model. Pattern Recogn. Lett. 133, 173–179 (2020)

    Article  Google Scholar 

  23. Wang, Y., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for image denoising. Multimed. Tools Appl. 78(14), 19945–19960 (2019)

    Article  Google Scholar 

  24. Ding, B., Long, C., Zhang, L., Xiao, C.: Argan: Attentive recurrent generative adversarial network for shadow detection and removal. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10213–10222, (2019)

  25. Wang, J., Li, X., Yang, J.: Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1788–1797, (2018)

Download references

Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant No:42271409) and the Scientific Research Foundation of the Higher Education Institutions of Liaoning Province (Grant No:LJKMZ20220699).

Author information

Authors and Affiliations

  1. School of Software, Liaoning Technical University, No.188 South Street of Longwan, Huludao, 125105, Liaoning Province, China

    Haicheng Qu, Chang Tong & Wanjun Liu

Authors
  1. Haicheng Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haicheng Qu.

Additional information

Publisher's Note

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

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

Qu, H., Tong, C. & Liu, W. Image shadow removal algorithm guided by progressive attention mechanism. SIViP 17, 2565–2571 (2023). https://doi.org/10.1007/s11760-022-02473-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02473-z

Keywords

Search

Navigation