Skip to main content
SpringerLink
Log in

Attention-guided network with hierarchical global priors for low-light image enhancement

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

Abstract

Low-light image enhancement technology is widely used extensively in industry, but it is also a challenging aspect of computer vision tasks. Although existing methods are committed to adjusting the overall brightness of the image, they ignore the problems of region excessive exposure and uneven illumination enhancement. They also introduce new problems, such as noise generation and tone distortion, during the process. To solve these problems, we propose a two-step deep learning-based method that combines a proposed attention-guided network with hierarchical global priors (GPANet) with a Mapping Curve function. In the proposed method, GPANet is first used as a deep curve estimation network to generate a self-adaptive enhanced mapping feature map. Subsequently, a pixel-wise curve is utilized to aid in the generation of natural tone-enhanced images. To evaluate the performance of the proposed method, we conducted comparative experiments with different methods on commonly used public datasets, and the proposed method achieved superior results in terms of naturalness image quality evaluator and lightness order error.

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
Fig. 6

Similar content being viewed by others

Notes

  1. https://sites.google.com/site/vonikakis/datasets

References

  1. Li, C., Guo, C., Han, L.H., Jiang, J., Cheng, M.M., Gu, J., Loy, C.C.: Low-light image and video enhancement using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 01, 1–1 (2021)

    Google Scholar 

  2. Abdullah-Al-Wadud, M., Kabir, M.H., Dewan, M.A.A., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(2), 593–600 (2007)

    Article  Google Scholar 

  3. Wang, S., Zheng, J., Hu, H.M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)

    Article  Google Scholar 

  4. Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2782–2790 (2016)

  5. Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3–6, 2018, p 155 (2018)

  7. Lv, F., Li, Y., Lu, F.: Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int. J. Comput. Vis. 129(7), 2175–2193 (2021)

    Article  Google Scholar 

  8. Kwon, D., Kim, G., Kwon, J.: Dale: Dark region-aware low-light image enhancement. arXiv preprint arXiv:2008.12493 (2020)

  9. Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780–1789 (2020)

  10. Tao, L., Zhu, C., Xiang, G., Li, Y., Jia, H., Xie, X.: Llcnn: A convolutional neural network for low-light image enhancement. In: 2017 IEEE Visual Communications and Image Processing (VCIP), IEEE, pp. 1–4 (2017)

  11. Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., Wang, Z.: Enlightengan: deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340–2349 (2021)

    Article  Google Scholar 

  12. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

  13. Zhou, S., Li, C., Loy, C.C.: Lednet: Joint low-light enhancement and deblurring in the dark. arXiv preprint arXiv:2202.03373 (2022)

  14. Zhao, Y., Jiang, Z., Men, A., Ju, G.: Pyramid real image denoising network. In: 2019 IEEE Visual Communications and Image Processing, VCIP 2019, Sydney, Australia, December 1–4, 2019, IEEE, pp. 1–4, (2019) https://doi.org/10.1109/VCIP47243.2019.8965754

  15. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13713–13722 (2021)

  16. Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3291–3300 (2018)

  17. Cai, J., Gu, S., Zhang, L.: Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans. Image Process. 27(4), 2049–2062 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  18. Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692–1700 (2018)

  19. Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1905–1914 (2021)

  20. Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Cycleisp: Real image restoration via improved data synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2696–2705 (2020)

  21. Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation. In: 2012 19th IEEE International Conference on Image Processing, IEEE, pp. 965–968 (2012)

  22. Ma, K., Zeng, K., Wang, Z.: Perceptual quality assessment for multi-exposure image fusion. IEEE Trans. Image Process. 24(11), 3345–3356 (2015)

  23. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind’’ image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2012)

    Article  Google Scholar 

  24. Wang, X., Chen, J., Wang, Z., Liu, W., Satoh, S., Liang, C., Lin, C.: When pedestrian detection meets nighttime surveillance: a new benchmark. In: Bessiere C (ed) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, ijcai.org, pp. 509–515 (2020). https://doi.org/10.24963/ijcai.2020/71

  25. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. CoRR abs/1804.02767 (2018). arXiv:1804.02767

  26. Zhang, S., Benenson, R., Schiele, B.: Citypersons: A diverse dataset for pedestrian detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21–26, 2017, IEEE Computer Society, pp. 4457–4465. https://doi.org/10.1109/CVPR.2017.474 (2017)

Download references

Acknowledgements

This work was supported in part by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-004, and in part by the Fundamental Research Funds for the Central Universities under Grant 20CX05019A.

Author information

Author notes

  1. An Gong and Zhonghao Li have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China

    An Gong & Zhonghao Li

  2. Qingdao OKet Technology Co., Ltd, Qingdao, 266555, China

    Heng Wang

  3. College of Software, Beihang University, Beijing, 100191, China

    Guangtong Li

Authors
  1. An Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhonghao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangtong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhonghao Li.

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

Gong, A., Li, Z., Wang, H. et al. Attention-guided network with hierarchical global priors for low-light image enhancement. SIViP 17, 2083–2091 (2023). https://doi.org/10.1007/s11760-022-02422-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02422-w

Keywords

Search

Navigation