Multimedia Tools and Applications

, Volume 75, Issue 18, pp 11221–11239 | Cite as

Sparse codes fusion for context enhancement of night video surveillance



Fusion-based method for video enhancement has been playing a basic but significant role, which is also proved high-efficiency. Still, there are some open questions, such as lamp-off problem, over-enhanced moving objects and night shadow. To resolve the problems, a novel method—sparse codes fusion (SCF) is proposed. With plenty of samples from daytime videos and nighttime videos of the same scene, we learn and obtain a daytime dictionary and a nighttime dictionary using the proposed mutual coherence learning (MCL) algorithm. These two dictionaries are utilized for fusion and extracting context enhanced background. Moreover, we reconstruct the nighttime dictionary to get nighttime background that would be applied in motion extraction. Then the moving objects are added into the enhanced background. Extensive experimental results show a highly comprehensive description of video frames that leads to improvements over the state of the art on many usual public video datasets.


Video enhancement Sparse codes fusion (SCF) Daytime dictionary Nighttime dictionary Mutual coherence learning (MCL) Enhanced background Motion extraction 



The authors would like to thank the anonymous reviewers for their helpful comments. This work is partly supported by National Science Foundation of China (Grant No. 61300092), Fundamental Research Funds for the Central Universities (Grant No. ZYGX2013J068), and Sichuan Province Science and Technology Support Program Project (Grant No. 2013GZ0151).


  1. 1.
    Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 55(11):4311–4322CrossRefGoogle Scholar
  2. 2.
    Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 9(18):1921–1935MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bennett EP, McMillan L (2005) Video enhancement using per-pixel virtual exposures. Proc ACM Trans Graph 24(3):845–852CrossRefGoogle Scholar
  4. 4.
    Cai YH et al (2006) Context enhancement of nighttime surveillance by image fusion. Proc. ICPR 2006, vol.1, pp 980–983Google Scholar
  5. 5.
    Cheng DY, Sun TF, Jiang XH (2013) A robust image classification scheme with sparse coding and multiple kernel learning. Digit Forensic Watermarking 7809:520–529CrossRefGoogle Scholar
  6. 6.
    Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic range images. ACM Trans Graph 21(3):257–266CrossRefGoogle Scholar
  7. 7.
    Harandi M (2012) Sparse coding and dictionary learning for symmetric positive definite matrices: a Kernel approach. European Conference on Computer Vision, pp 216–229Google Scholar
  8. 8.
    Ilie A, Raskar R, Yu JY (2005) Gradient domain context enhancement for fixed cameras. Int J Pattern Recognit Artif Intell 4(19):533–549CrossRefGoogle Scholar
  9. 9.
    Ji Q, Yu S (2013) Motion object detection based on adaptive mixture Gaussian model and four-frame subtraction. International Conference on Computational and Information Sciences, pp 1202–1205Google Scholar
  10. 10.
    Lee C et al (2012) Power-constrained contrast enhancement for emissive displays based on histogram equalization. IEEE Trans Image Process 21(1):80–93MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lin W, Sun M-T, Poovendran R, Zhang Z (2010) Group event detection with a varying number of group members for video surveillance. IEEE Trans Circuits Syst Video Technol 8(20):1057–1067CrossRefGoogle Scholar
  12. 12.
    Paris S, Halkias X, Glotin H (2012) Sparse coding for histograms of local binary patterns applied for image categorization: toward a bag-of-scenes analysis. International Conference on Pattern Recognition, pp 2817–2820Google Scholar
  13. 13.
    Rao YB, Chen LT (2012) A survey of video enhancement techniques. Int J Electr Eng Inform 3(1):71–99Google Scholar
  14. 14.
    Rao YB, Hou L, Wang ZH, Chen LT (2012) Illumination-based nighttime video contrast enhancement using genetic algorithm. Multimedia Tools Appl 70(3):2235–2254CrossRefGoogle Scholar
  15. 15.
    Rao YB, Zhang YH, Gou JP (2013) Gradient fusion method for night video enhancement. ETRI J 35(5):923–926CrossRefGoogle Scholar
  16. 16.
    Raskar R, Ilie A, Yu JY (2005) Image fusion for context enhancement and video surrealism. Proc. SIGGRAPH 2005. ACM, New YorkGoogle Scholar
  17. 17.
    Saponara S, Fanucci L, Petri E (2013) A multi-processor NoC-based architecture for real-time image/video enhancement. J Real-Time Image Proc 8(1):111–125CrossRefGoogle Scholar
  18. 18.
    Tropp JA (2004) Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory 50(10):2231–2242MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Tsai CY (2012) A fast dynamic range compression with local contrast preservation algorithm and its Application to real-time video enhancement. IEEE Trans Multimedia 14(4):1140–1152CrossRefGoogle Scholar
  20. 20.
    Vollmer C, Gross HM, Eggert JP (2013) Learning features for activity recognition with shift-invariant sparse coding. Artif Neural Netw Mach Learn 8131:367–374Google Scholar
  21. 21.
    Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted threshold histogram equalization. IEEE Trans Consum Electron 53(2):757–764CrossRefGoogle Scholar
  22. 22.
    Yamasaki A et al (2008) Denighting: enhancement of nighttime image for a surveillance camera. 19th Int. Conf. Pattern RecogGoogle Scholar
  23. 23.
    Zhu QD, Jing LQ, Bi RS (2010) Exploration and improvement of Ostu threshold segmentation algorithm. IEEE World Congress on Intelligent Control and Automation, pp 6183–6188Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Computer Science & EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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