Application of Daubechies Wavelet Transformation for Noise Rain Reduction on the Video

Siti Khotijah, Dwi Ratna Sulistyaningrum


Currently, the use of digital video in the field of computer science is increasingly widespread, such as the process of tracking objects, the calculation of the number of vehicles, the classification of vehicle types, vehicle speed estimation and so forth. The process of taking digital video is often influenced by bad weather, such rain. Rain in digital video is considered noise because it is able to block objects being observed. Therefore, a rainfall noise reduction process is required in the video. In this study, the reduction of rain noise in digital video is using Daubechies wavelet transformation through several processes, namely: wavelet decomposition, fusion process, thresholding process and reconstruction process. The threshold value used in the thresholding process is VishuShrink, BayesShrink, and NormalShrink. The result of the implementation and noise reduction test show that Daubechies db2 level 3 filter gives the result with the biggest PSNR value. As for the type of threshold that provides optimal results is VishuShrink.


Daubechies Wavelet Transformation; fusion; VishuShrink; BayesShrink; NormalShrink

Full Text:



K. Garg and S. K. Nayar, “Detection and removal of rain from videos,” in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 1, 2004, pp. I–I.

X. Zhang, H. Li, Y. Qi, W. K. Leow, and T. K. Ng, “Rain removal in video by combining temporal and chromatic properties,” in IEEE International Conference on Multimedia and Expo, 2006, pp. 461–464.

N. Gupta, “Video modeling and noise reduction in the wavelet domain,” Ph.D. dissertation, Concordia University, 2011.

C. Zhen and S. Jihong, “A new algorithm of rain (snow) removal in video.” Journal of Multimedia, vol. 8, no. 2, 2013.

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE transactions on pattern analysis and machine intelligence, vol. 11, no. 7, pp. 674–693, 1989.

G. Pajares and J. M. De La Cruz, “A wavelet-based image fusion tutorial,” Pattern recognition, vol. 37, no. 9, pp. 1855–1872, 2004.

Z. Zhang and R. S. Blum, “A categorization of multiscaledecomposition-based image fusion schemes with a performance study for a digital camera application,” Proceedings of the IEEE, vol. 87, no. 8, pp. 1315–1326, 1999.

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” biometrika, vol. 81, no. 3, pp. 425–455, 1994.

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE transactions on image processing, vol. 9, no. 9, pp. 1532–1546, 2000.

M. Kaur, K. Sharma, and N. Dhillon, “Image denoising using wavelet thresholding,” International Journal of Engineering and Computer Science, vol. 2, no. 10, 2013.

M. Vranjes, S. Rimac-Drlje, and K. Grgic, “Locally averaged psnr as a simple objective video quality metric,” in 50th International Symposium ELMAR, vol. 1, 2008, pp. 17–20.



  • There are currently no refbacks.

View My Stats

Creative Commons License
International Journal of Computing Science and Applied Mathematics by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at