Skip to main content
SpringerLink
Log in

Computer Vision Approach for Visibility Enhancement of Dull Images

  • Conference paper
  • First Online:
Innovations in Electrical and Electronic Engineering (ICEEE 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 894))

Included in the following conference series:

  • 669 Accesses

Abstract

Most of the Computer Vision tasks emphasizes images with a standard level of illumination. Adversely, on the pragmatic ground, the applications are often challenged with low light input images. Howbeit, a low vision image captured under poor light conditions is prone to suffer from a considerable redundancy of valuable information. This often makes the image unsuitable for performing any computational task, and hence requires to be radiated and restored. To deal with this problem, in this paper, we discuss Computer Vision-based low vision image enhancement methods and analyse their accuracy. This paper stresses the approach of Histogram Equalization for visibility enhancement. However, this method was found to be inconsistent with actual relics within an image. Hence, furthermore we discuss another approach based on Dual Channel Prior for enhanced outcomes. A comprehensive study of these methods along with their performance and efficacy has been demonstrated in this paper and further research orientations in this work area have been proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shi, Z., Zhu, M., Guo, B., Zhao, M., Zhang, C.: Nighttime low illumination image enhancement with single image using bright/dark channel prior. EURASIP J. Image Video Process. 2018(1), 1–15 (2018). https://doi.org/10.1186/s13640-018-0251-4

    Article  Google Scholar 

  2. Wang, W., Wu, X., Yuan, X., Gao, Z.: An experiment-based review of low-light image enhancement methods. IEEE Access 8, 87884–87917 (2020)

    Article  Google Scholar 

  3. Yu, S.-Y., Zhu, H.: Low-illumination image enhancement algorithm based on a physical lighting model. IEEE Trans. Circ. Syst. Video Technol. 29(1), 28–37 (2017)

    Article  Google Scholar 

  4. 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  Google Scholar 

  5. Zhou, Z., Sang, N., Hu, X.: Global brightness and local contrast adaptive enhancement for low illumination color image. Optik 125(6), 1795–1799 (2014)

    Article  Google Scholar 

  6. Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.-P., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965–4977 (2015)

    Article  MathSciNet  Google Scholar 

  7. Xu, Y., Yang, C., Sun, B., Yan, X., Chen, M.: A novel multi-scale fusion framework for detail-preserving low-light image enhancement. Inf. Sci. 548, 378–397 (2021)

    Article  MathSciNet  Google Scholar 

  8. Cai, L., Qian, J.: Night color image enhancement using fuzzy set. In: 2009 2nd International Congress on Image and Signal Processing, pp. 1–4. IEEE (2009)

    Google Scholar 

  9. Ren, W., et al.: Low-light image enhancement via a deep hybrid network. IEEE Trans. Image Process. 28(9), 4364–4375 (2019)

    Article  MathSciNet  Google Scholar 

  10. Cheng, H.D., Shi, X.J.: A simple and effective histogram equalization approach to image enhancement. Digit. Signal Process. 14(2), 158–170 (2004)

    Article  Google Scholar 

  11. Senthilkumaran, N., Thimmiaraja, J.: Histogram equalization for image enhancement using MRI brain images. In: 2014 World Congress on Computing and Communication Technologies, pp. 80–83. IEEE (2014)

    Google Scholar 

  12. Lee, H., Sohn, K., Min, D.: Unsupervised low-light image enhancement using bright channel prior. IEEE Signal Process. Lett. 27, 251–255 (2020)

    Article  Google Scholar 

  13. Lee, S., Yun, S., Nam, J.-H., Won, C.S., Jung, S.-W.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 2016(1), 1–23 (2016)

    Article  Google Scholar 

  14. Park, S., Yu, S., Moon, B., Ko, S., Paik, J.: Low-light image enhancement using variational optimization-based retinex model. IEEE Trans. Consum. Electron. 63(2), 178–184 (2017)

    Article  Google Scholar 

  15. Sandoub, G., Atta, R., Ali, H.A., Abdel-Kader, R.F.: A low-light image enhancement method based on bright channel prior and maximum colour channel. IET Image Process. 15, 1759–1772 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology Agartala, Agartala, India

    Chandrika Acharjee & Suman Deb

Authors
  1. Chandrika Acharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suman Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandrika Acharjee .

Editor information

Editors and Affiliations

  1. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia

    Saad Mekhilef

  2. Office of the International Relations, Bharath Institute of Higher Education and Research, Chennai, India

    Rabindra Nath Shaw

  3. Department of Management and Innovation Systems, University of Salerno, Fisciano, Salerno, Italy

    Pierluigi Siano

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Acharjee, C., Deb, S. (2022). Computer Vision Approach for Visibility Enhancement of Dull Images. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 894. Springer, Singapore. https://doi.org/10.1007/978-981-19-1677-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1677-9_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1676-2

  • Online ISBN: 978-981-19-1677-9

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation