Skip to main content
SpringerLink
Log in

Recolorizing dark regions to enhance night surveillance video

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Security surveillance cameras are widely deployed to ensure secure banking, entertainment, and assisted living. Surveillance videos captured by these cameras are considered as forensic evidence for detecting crimes such as ATM robbery and vehicle theft. The videos captured under low lighting conditions are insufficient to identify a theft or robbery happened in the dark regions of a surveillance area. In this paper, we propose a recolorization based night video enhancement to increase the visual perception of surveillance videos. The day background illumination and tone adjusted night video frames are combined to reduce the darkness of the night video frame. Subsequently, chromatic colors of the day image regions are selected corresponding to the dark regions of night frame for the optimization based colorization by using white edge scribbles. The proposed algorithm significantly enhanced the perceptual quality of the video frames compared with existing algorithms. The no-reference based objective evaluation approaches are used for comparing and evaluating the performance of the proposed method with the existing methods. The experimental results indicated that the method improved the visual perception of the night surveillance video compared to the existing methods.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Agrawal A, Raskar R, Nayar SK, Li Y (2005) Removing photography artifacts using gradient projection and flash-exposure sampling. ACM Trans Graph 24 (3):828–835

    Article  Google Scholar 

  2. Canny J (1986) Computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Chouhan R, Jha RK, Biswas PK (2013) Enhancement of dark and low-contrast images using dynamic stochastic resonance. IET Image Process 7(2):174–184

    Article  MathSciNet  Google Scholar 

  5. Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Proc. SPIE 6064, Image processing: Algorithm and systems, Neural Networks and machine learning, 606414

  6. Honda H, Timofte R, Van Gool L (2015) Make my day-high-fidelity color denoising with near-infrared. In: Computer vision and pattern recognition workshops (CVPRW), 2015 IEEE Conference on. IEEE, pp 82–90

  7. http://www.live.ece.utexas.edu/. Accessed: 08-06-2015

  8. http://www.taoyangjingli.net/data. Accessed: 11-05-2007

  9. Irony R, Cohen-Or D, Lischinski D (2005) Colorization by example. In: Eurographics Symposium on Rendering. Citeseer, vol 2

  10. Kirk AG, O’Brien JF (2011) Perceptually based tone mapping for low-light conditions. ACM Trans Graph 30(4):42

    Article  Google Scholar 

  11. Lai YR, Tsai PC, Yao CY, Ruan SJ (2015) Improved local histogram equalization with gradient-based weighting process for edge preservation. Multimed Tools Appl:1–29

  12. Lee S (2007) An efficient content-based image enhancement in the compressed domain using retinex theory. IEEE Trans Circuits Syst Video Technol 17(2):199–213

    Article  MathSciNet  Google Scholar 

  13. Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: ACM transactions on graphics (TOG). ACM, vol. 23, pp 689–694

  14. Li J, Li SZ, Pan Q, Yang T (2005) Illumination and motion-based video enhancement for night surveillance. 2nd Joint IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance

  15. Łoza A, Bull DR, Hill PR, Achim AM (2013) Automatic contrast enhancement of lowlight images based on local statistics of wavelet coefficients. Digital Signal Process 23(6):1856–1866

    Article  Google Scholar 

  16. McCann J, Funt B, Ciurea F (2000) Retinex in matlab. Proc. IS&T/SID 8th Color Imaging Conf

  17. Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Article  MathSciNet  MATH  Google Scholar 

  18. Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Processing Lett 20(3):209–212

    Article  Google Scholar 

  19. Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Process Lett 17(5):513–516

    Article  Google Scholar 

  20. Petit J, Brémond R (2010) A high dynamic range rendering pipeline for interactive applications. Vis Comput 26(6-8):533–542

    Article  Google Scholar 

  21. Rao Y, Lin W, Chen L (2010) Image-based fusion for video enhancement of night-time surveillance. Opt Eng 49(12):120,501–120,501

    Article  Google Scholar 

  22. Reza AM (2004) Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology 38(1):35–44

    Article  Google Scholar 

  23. Soumya T, Thampi SM (2015) Day color transfer based night video enhancement for surveillance system, vol 1

  24. Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896

    Article  Google Scholar 

  25. T Soumya, Thampi SM (2016) Self-organized night video enhancement for surveillance systems. SIViP:1–8

  26. Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of jpeg compressed images. Int Conf Image Process 1:I–477

    Google Scholar 

  27. Xu Q, Jiang H, Scopigno R, Sbert M (2014) A novel approach for enhancing very dark image sequences. Signal Process 103:309–330

    Article  Google Scholar 

  28. Yamasaki A, Takauji H, Kaneko S, Kanade T, Ohki H (2008) Denighting: Enhancement of nighttime images for a surveillance camera. 19th international conference on pattern recognition (ICPR) pp 1–4

  29. Yatziv L, Sapiro G (2006) Fast image and video colorization using chrominance blending. IEEE Trans Image Process 15(5):1120–1129

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank Center for Engineering Research and Development (CERD), College of Engineering Trivandrum for research facilities and Tao Yang for sharing databases.

Author information

Authors and Affiliations

  1. College of Engineering Trivandrum, LBS Instiute of Science and Technologoly, Thiruvananthapuram, Kerala, 695016, India

    Soumya T.

  2. Indian Institute of Information Technology and Management-kerala, Thiruvananthapuram, Kerala, 695581, India

    Sabu M. Thampi

Authors
  1. Soumya T.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabu M. Thampi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumya T..

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soumya T., Thampi, S.M. Recolorizing dark regions to enhance night surveillance video. Multimed Tools Appl 76, 24477–24493 (2017). https://doi.org/10.1007/s11042-016-4141-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4141-4

Keywords

Search

Navigation