Skip to main content
SpringerLink
Log in

Low-light image enhancement network based on multi-stream information supplement

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Images captured under low-light conditions often suffer from severe loss of structural details and color; therefore, image-enhancement algorithms are widely used in low-light image restoration. Image-enhancement algorithms based on the traditional Retinex model only consider the change in the image brightness, while ignoring the noise and color deviation generated during the process of image restoration. In view of these problems, this paper proposes an image enhancement network based on multi-stream information supplement, which contains a mainstream structure and two branch structures with different scales. To obtain richer feature information, an information complementary module is designed to realize the information supplement for the three structures. The feature information from the three structures is then concatenated to perform the final image recovery operation. To restore more abundant structures and realistic colors, we define a joint loss function by combining the L1 loss, structural similarity loss, and color-difference loss to guide the network training. The experimental results show that the proposed network achieves satisfactory performance in both subjective and objective aspects.

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

  • Afifi, M., Derpanis, K. G., Ommer, B., & Brown, M. S. (2021). Learning multi-scale photo exposure correction. In Proc. IEEE int. conf. comput. vision pattern recognit. (pp. 9157–9167).

  • Dong, X., Wang, G., Pang, Y., Li, W., Wen, J., Meng, W., & Lu, Y. (2011). Fast efficient algorithm for enhancement of low lighting video. In Proc. IEEE int. conf. multimedia expo. (pp. 1–6).

  • Duc, T., Dang, N., Cecilia, P., Valentina, C., & Giulia, B. (2015). Raise: a raw images dataset for digital image forensics. In Proc. 6th ACM multimedia syst. conf. (pp. 219–224).

  • Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., & John, P. (2016). A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129, 82–96.

    Article  Google Scholar 

  • Guo, X., Li, Y., & Ling, H. (2017). LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2), 982–993.

    Article  MathSciNet  Google Scholar 

  • Guo, Y., Ke, X., Ma, J., & Zhang, J. (2019). A pipeline neural network for low-light image enhancement. IEEE Access, 7, 13737–13744.

    Article  Google Scholar 

  • Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021). EnlightenGAN: Deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30, 2340–2349.

    Article  Google Scholar 

  • Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997a). Properties and performance of a center/surround Retinex. IEEE Transactions on Image Processing, 6(3), 451–462.

    Article  Google Scholar 

  • Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997b). A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7), 965–976.

    Article  Google Scholar 

  • Ko, S., Yu, S., Kang, W., Park, C., Lee, S., & Paik, J. (2017). Artifact-free low-light video enhancement using temporal similarity and guide map. IEEE Transactions on Industrial Electronics, 64(8), 6392–6401.

    Article  Google Scholar 

  • Land, E. H. (1977). The Retinex theory of color vision. Scientific American, 237(6), 108–128.

    Article  MathSciNet  Google Scholar 

  • Lee, H., Sohn, K., & Min, D. (2020). Unsupervised low-light image enhancement using bright channel prior. IEEE Signal Processing Letters, 27, 251–255.

    Article  Google Scholar 

  • Li, L., Wang, R., Wang, W., & Gao, W. (2015). A low-light image enhancement method for both denoising and contrast enlarging In Proc. IEEE int. conf. image process. (pp. 3730–3734).

  • Park, S., Moon, B., Ko, S., Yu, S., & Paik, J. (2017). Low-light image enhancement using variational optimization-based Retinex model. In Proc. IEEE int. conf. consum. electron. (pp. 70–71).

  • Rao, Y., & Chen, L. (2012). A survey of video enhancement techniques. Journal of Information Hiding and Multimedia Signal Processing, 3(1), 71–99.

    Google Scholar 

  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Proc. IEEE int. conf. medical image comput. comput.—Assisted intervention (pp. 234–241).

  • Sharma, G., Wu, W., & Dalal, E. (2005). The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(1), 21–30.

    Article  Google Scholar 

  • Sujee, R., & Padmavathi, S. (2017). Image enhancement through pyramid histogram matching. In Proc. int. conf. comput. commun. inform. (pp. 1–5).

  • Tao, L., Zhu, C., Song, J., Lu, T., Jia, H., & Xie, X. (2017). Low-light image enhancement using CNN and bright channel prior. In Proc. IEEE int. conf. image process. (pp. 3215–3219).

  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(11), 600–612.

    Article  Google Scholar 

  • Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. In Proc. IEEE conf. rec. 37th Asilomar conf. signals syst. comput. (Vol. 2, pp. 1398–1402).

  • Wei, C., Wang, W., Yang, W., & Liu, J. (2018). Deep Retinex decomposition for low-light enhancement. In Proc. British mach. vis. conf. (pp. 1–10).

  • Yang, M., Tang, G., Liu, X., Wang, L., Cui, Z., & Luo, S. (2018). Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform. Optoelectronics Letters, 14(6), 470–475.

    Article  Google Scholar 

  • Ying, Z., Li, G., Gao, W. (2017). A bio-inspired multi-exposure fusion framework for low-light image enhancement. arXiv:1711.00591

  • Yu, S., & Zhu, H. (2019). Low-illumination image enhancement algorithm based on a physical lighting model. IEEE Transactions on Circuits and Systems for Video Technology, 29(1), 28–37.

    Article  Google Scholar 

  • Yu, X., Luo, X., Lyu, G., & Luo, S. (2017). A novel Retinex based enhancement algorithm considering noise. In Proc. 16th int. conf. comput. inf. sci. (pp. 649–654).

  • Zhang, Y., Zhang, J., & Guo, X. (2019). Kindling the darkness: A practical low-light image enhancer. In Proc. ACM multimedia. (pp. 1632–1640).

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61862030 and 62072218), by the Natural Science Foundation of Jiangxi Province (Nos. 20182BCB22006, 20181BAB202010, 20192ACB20002, and 20192ACBL21008), and by the Talent project of Jiangxi Thousand Talents Program (No. jxsq2019201056).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Yong Yang

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Yong Yang, Wei Hu & Wei Tu

  3. School of Software, Tiangong University, Tianjin, 300387, China

    Shuying Huang

  4. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Weiguo Wan

Authors
  1. Yong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weiguo Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuying Huang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Hu, W., Huang, S. et al. Low-light image enhancement network based on multi-stream information supplement. Multidim Syst Sign Process 33, 711–723 (2022). https://doi.org/10.1007/s11045-021-00812-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-021-00812-w

Keywords

Search

Navigation