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Multimedia Tools and Applications

, Volume 76, Issue 22, pp 24477–24493 | Cite as

Recolorizing dark regions to enhance night surveillance video

  • Soumya T.Email author
  • Sabu M. Thampi
Article
  • 263 Downloads

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.

Keywords

Night video surveillance Video enhancement Colorization No-Reference objective quality measure 

Notes

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.

References

  1. 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):828–835CrossRefGoogle Scholar
  2. 2.
    Canny J (1986) Computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698CrossRefGoogle Scholar
  3. 3.
    Cheng H, Shi X (2004) A simple and effective histogram equalization approach to image enhancement. Digital Signal Process 14(2):158–170CrossRefGoogle Scholar
  4. 4.
    Chouhan R, Jha RK, Biswas PK (2013) Enhancement of dark and low-contrast images using dynamic stochastic resonance. IET Image Process 7(2):174–184CrossRefMathSciNetGoogle Scholar
  5. 5.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Proc. SPIE 6064, Image processing: Algorithm and systems, Neural Networks and machine learning, 606414Google Scholar
  6. 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 82–90Google Scholar
  7. 7.
    http://www.live.ece.utexas.edu/. Accessed: 08-06-2015
  8. 8.
    http://www.taoyangjingli.net/data. Accessed: 11-05-2007
  9. 9.
    Irony R, Cohen-Or D, Lischinski D (2005) Colorization by example. In: Eurographics Symposium on Rendering. Citeseer, vol 2Google Scholar
  10. 10.
    Kirk AG, O’Brien JF (2011) Perceptually based tone mapping for low-light conditions. ACM Trans Graph 30(4):42CrossRefGoogle Scholar
  11. 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:1–29Google Scholar
  12. 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):199–213CrossRefMathSciNetGoogle Scholar
  13. 13.
    Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: ACM transactions on graphics (TOG). ACM, vol. 23, pp 689–694Google Scholar
  14. 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 surveillanceGoogle Scholar
  15. 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):1856–1866CrossRefGoogle Scholar
  16. 16.
    McCann J, Funt B, Ciurea F (2000) Retinex in matlab. Proc. IS&T/SID 8th Color Imaging ConfGoogle Scholar
  17. 17.
    Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708CrossRefMathSciNetzbMATHGoogle Scholar
  18. 18.
    Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Processing Lett 20(3):209–212CrossRefGoogle Scholar
  19. 19.
    Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Process Lett 17(5):513–516CrossRefGoogle Scholar
  20. 20.
    Petit J, Brémond R (2010) A high dynamic range rendering pipeline for interactive applications. Vis Comput 26(6-8):533–542CrossRefGoogle Scholar
  21. 21.
    Rao Y, Lin W, Chen L (2010) Image-based fusion for video enhancement of night-time surveillance. Opt Eng 49(12):120,501–120,501CrossRefGoogle Scholar
  22. 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):35–44CrossRefGoogle Scholar
  23. 23.
    Soumya T, Thampi SM (2015) Day color transfer based night video enhancement for surveillance system, vol 1Google Scholar
  24. 24.
    Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896CrossRefGoogle Scholar
  25. 25.
    T Soumya, Thampi SM (2016) Self-organized night video enhancement for surveillance systems. SIViP:1–8Google Scholar
  26. 26.
    Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of jpeg compressed images. Int Conf Image Process 1:I–477Google Scholar
  27. 27.
    Xu Q, Jiang H, Scopigno R, Sbert M (2014) A novel approach for enhancing very dark image sequences. Signal Process 103:309–330CrossRefGoogle Scholar
  28. 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 1–4Google Scholar
  29. 29.
    Yatziv L, Sapiro G (2006) Fast image and video colorization using chrominance blending. IEEE Trans Image Process 15(5):1120–1129CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.College of Engineering TrivandrumLBS Instiute of Science and TechnologolyThiruvananthapuramIndia
  2. 2.Indian Institute of Information Technology and Management-keralaThiruvananthapuramIndia

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