Skip to main content
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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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. In Computer 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 In IEEE, 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

Authors

Corresponding author

Correspondence to Fathi E. Abd El-Samie.

Rights and permissions

Reprints 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 Commun 99, 619–636 (2018). https://doi.org/10.1007/s11277-017-4958-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4958-9

Keywords

Navigation