Abstract
Rain streaks in videos change the intensities of pixels and affect the performance of outdoor vision systems. This paper proposes a rain removal method that can recover videos degraded by rain. This method exploits the fact that all rain streaks in a video have similar directions. First a dictionary is learnt for sparse representation of frame differences. Then the gradient distribution of the intensities of the atoms in this dictionary is analyzed to distinguish rain atoms and non-rain atoms. At last, frames are recovered by only using the non-rain atoms to represent frame differences. Experiments on real videos show the effectiveness of this method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barnum PC, Narasimhan S, Kanade T (2010) Analysis of rain and snow in frequency space. Int J Comput Vis 86(2–3):256–274
Brewer N, Liu NJ (2008) Using the shape characteristics of rain to identify and remove rain from video. In: Lecture notes in computer science, vol 5342/2008. Springer, Berlin, pp 451–458
Garg K, Nayar SK (2004) Detection and removal of rain from videos. In: Proceedings of the international conference on computer vision and pattern recognition, IEEE, pp 528–535
Zhang XP, Li H, Qi YY, Leow WK, Ng TK (2006) Rain removal in video by combining temporal and chromatic properties. In: Proceedings of the international conference on multimedia and expo, IEEE, pp 461–464
Hase H, Miyake K, Yoneda M (1999) Real-time snowfall noise elimination. In: Proceedings of the international conference on image processing, IEEE, pp 406–409
Kang LW, Lin CW, Fu YH (2012) Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process (TIP) 21(4):1742–1755
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 886–893
Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60
Wu J, Rehg JM (2011) CENTRIST: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell 33(8):1489–1501
Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415
Acknowledgments
This work was supported by the National Natural Science Foundation of China (51179146) and the Fundamental Research Funds for the Central Universities (2012-IV-041).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, G., Sun, Y., Chen, X. (2013). A Method for Removing Rain from Videos. In: Lu, W., Cai, G., Liu, W., **ng, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34531-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-34531-9_50
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34530-2
Online ISBN: 978-3-642-34531-9
eBook Packages: EngineeringEngineering (R0)