Skip to main content
SpringerLink
Log in

A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework

  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

Included in the following conference series:

Abstract

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. In this paper, we propose an image contrast enhancement algorithm to provide an accurate contrast enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image under-exposed. Finally, the input image and the synthetic image are fused according to the weight matrix to obtain the enhancement result. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

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

References

  1. Aydin, T.O., Mantiuk, R., Myszkowski, K., Seidel, H.P.: Dynamic range independent image quality assessment. ACM Trans. Graph. (TOG) 27(3), 69 (2008)

    Article  Google Scholar 

  2. Beghdadi, A., Le Negrate, A.: Contrast enhancement technique based on local detection of edges. Comput. Vis. Graph. Image Process. 46(2), 162–174 (1989)

    Article  Google Scholar 

  3. Chen, S.D., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49(4), 1310–1319 (2003)

    Article  Google Scholar 

  4. Dong, X., Wang, G., Pang, Y., Li, W., Wen, J., Meng, W., Lu, Y.: Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2011)

    Google Scholar 

  5. Guo, X.: Lime: a method for low-light image enhancement. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 87–91. ACM (2016)

    Google Scholar 

  6. Ibrahim, H., Kong, N.S.P.: Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(4), 1752–1758 (2007)

    Article  Google Scholar 

  7. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  8. Karaduzovic-Hadziabdic, K., Telalovic, J.H., Mantiuk, R.: Subjective and objective evaluation of multi-exposure high dynamic range image deghosting methods (2016)

    Google Scholar 

  9. Lee, C.H., Shih, J.L., Lien, C.C., Han, C.C.: Adaptive multiscale retinex for image contrast enhancement. In: 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 43–50. IEEE (2013)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. Peli, E.: Contrast in complex images. JOSA A 7(10), 2032–2040 (1990)

    Article  Google Scholar 

  12. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35–44 (2004)

    Article  Google Scholar 

  13. Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consum. Electron. 51(4), 1326–1334 (2005)

    Article  Google Scholar 

  14. 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 

  15. Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. (TOG) 31(6), 139 (2012)

    Google Scholar 

  16. Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: A new low-light image enhancement algorithm using camera response model, manuscript submitted for publication (2017)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the grant of National Science Foundation of China (No.U1611461), Shenzhen Peacock Plan (20130408-183003656), and Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001 and No. 2014B010117007).

Author information

Authors and Affiliations

  1. School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China

    Zhenqiang Ying, Ge Li, Yurui Ren, Ronggang Wang & Wenmin Wang

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Yurui Ren

Authors
  1. Zhenqiang Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ge Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yurui Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ronggang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ge Li .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W. (2017). A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64698-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64697-8

  • Online ISBN: 978-3-319-64698-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation