Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Enhancement of Infrared Images Based on Efficient Histogram Processing

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The objective of any night vision system is to enable a person to see in the dark. A low-contrast image puts a contrast constraint on the human observer visibility at night. This is the basic reason for the large number of accidents at night. This research presents two proposed approaches to enhance the visibility of the infrared (IR) night vision images through an efficient histogram processing. The first approach is based on contrast limited adaptive histogram equalization. The second proposed approach depends on histogram matching. The histogram matching uses a reference visual image for converting night vision images into good quality images. The obtained results are evaluated with quality metrics such as entropy, average gradient, contrast improvement factor and sobel edge magnitude.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Hollnagel, E., & Kallhammer, J. E. (2003).Effects of a night vision enhancement system (NVES) on driving: Results from a simulator study. Driving Assessment Conference, University of Linkoping, Autoliv Research, Sweden.

  2. Grag, R., Mittal, B., & Grag, S. (2011). Histogram equalization techniques for image enhancement.International Journal of Electronics & Communication Technology,2(1), 107–111.

    Google Scholar 

  3. Rabin, J., & Wiley, R. (1994). Switching from forward-looking infrared to night-vision goggles: Transitory effects on visual resolution.Aviation, Space, and Environmental Medicine,65, 327–329.

    Google Scholar 

  4. Zimmerman, J. B., Pizer, S. M., Staab, E. V., Perry, J. R., McCartney, W., & Brenton, B. C. (1988). An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. Report Number: WUCS-87-31 (1987).All Computer Science and Engineering Research.http://openscholarship.wustl.edu/cse_research/817.

  5. Gonzalez, R., & Wood, R. (2009).Digital image processing (Vol. 3). London: Pearson Education.

    Google Scholar 

  6. LCEO Night Vision Equipment. (2003). The principles of Night Vision.http://www.squonk.net/users/lceo/NVworks.htm.

  7. Hel-Or, Y., Hel-Or, H., & David, E. (2011). Fast template matching in non-linear tone-mapped images. InComputer vision (ICCV), international conference on IEEE (pp. 1355–1362).

  8. Russ, J. C. (2007).The image processing handbook (5rd edn.). CRC Press.

  9. Rolland, J. P., Vo, V., Bloss, B., & Abbey, C. K. (2000). Fast algorithms for histogram matching: Application To texture synthesis.Journal of Electronic Imaging,9(1), 39–45.

    Article  Google Scholar 

  10. Shome, S. K., & Vadali, S. R. K. (2011). Enhancement of Di-abetic retinopathy imagery using contrast limited adaptive histogram equalization.International Journal of Computer Science and Information Technologies,2(6), 2694–2699.

    Google Scholar 

  11. Stark, J. A. (2000). Adaptive image contrast enhancement using generalizations of histogram equalization.IEEE Transactions on Image Processing,9(5), 889–894. doi:https://doi.org/10.1109/83.841534.

    Article  Google Scholar 

  12. Zhiming, W., & Jianhua, T. (2006). A Fast implementation of adaptive histogram equalization InIEEE, ICSP proceedings.

  13. Yoo, J., Ohm, S., & Chung, M. (2012). Maximum-entropy image enhancement using brightness mean and variance.Journal of Korean Society for Internet Information,13(3), 61–73.

    Google Scholar 

  14. Renukalatha, S., & Suresh, K. V. (2016). Brain tumor analysis of Rician noise affected MRI images.International Journal of Computer Applications,141(14), 0975–8887.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Communications, Bilbis Higher Institute of Engineering, Bilbis, Sharqia, Egypt

    H. I. Ashiba

  2. Department of Electronics and Electrical Communications, Faculty of Shoubra Engineering, Banha University, Banha, Egypt

    H. M. Mansour & H. M. Ahmed

  3. Department of Electronics and Electrical Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    M. F. El-Kordy, M. I. Dessouky & Fathi E. Abd El-Samie

Authors
  1. H. I. Ashiba

    You can also search for this author inPubMed Google Scholar

  2. H. M. Mansour

    You can also search for this author inPubMed Google Scholar

  3. H. M. Ahmed

    You can also search for this author inPubMed Google Scholar

  4. M. F. El-Kordy

    You can also search for this author inPubMed Google Scholar

  5. M. I. Dessouky

    You can also search for this author inPubMed Google Scholar

  6. Fathi E. Abd El-Samie

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toFathi E. Abd El-Samie.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashiba, H.I., Mansour, H.M., Ahmed, H.M.et al. Enhancement of Infrared Images Based on Efficient Histogram Processing.Wireless Pers Commun99, 619–636 (2018). https://doi.org/10.1007/s11277-017-4958-9

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp