A Video Denoising Method with 3D Surfacelet Transform Based on Block matching and Grouping

doi:10.4304/jcp.7.5.1130-1134

Journal of Computers, Vol 7, No 5 (2012), 1130-1134, May 2012

doi:10.4304/jcp.7.5.1130-1134

A Video Denoising Method with 3D Surfacelet Transform Based on Block matching and Grouping

Peng Geng, He Jiang, Zhigang Zhang, Xiang Zheng

Abstract

This paper proposes a novel video denoising method combining block matching based on the E3SS and grouping these blok strategy, 3D Surfacelet transform. Firstly, we utilize the SAD standard and E3SS search algorithm which we proposed by searching all frames for blocks which are similar to the currently processed one. Secondly, the matched blocks are stacked together to form some new 3D Sub-video sequence and because of the similarity between them, the data in the video array exists high level of correlation. We apply the 3D surfacelet transform to them and effectively attenuate the noise by solid threshold shrinkage of the 3D transform coefficients. Finally, inversely transforming the coefficients and obtaining the denoising video. This algorithm is obviously better than other 3D method in the denoising effect and the PSNR is increased about 0.9 dB. In terms of visual quality, the proposed method can effectively preserve the video detail, and the trajectory of motion object is very smooth, which is especially adequate to process the video flames with acute movement and plenty of large area movement object and background movement.

Keywords

video denoising;Surfacelet transform;Block matching;Grouping

References

[1] E Chang, A Zakhor, Subband video coding based on velocity flters, IEEE International Symposium on Circuits and Systems. San Diego, USA: Academic Press, 1992:2288-2291.

[2] I W Selesnick, K Y Li, Video denoising using 2D and 3D dual tree complex wavelet transforms, Proc.of SPIE conference on Wavelet Applications in Signaland Image Processing X. San Diego, USA: Academic Press, vol. 5207, pp. 607-618,2003.

[3] Yang, Jingyu, Wang Yao, Xu Wenli, Dai Qionghai, Image and video denoising using adaptive dual-tree discrete wavelet packets, IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, pp. 642-655, May 2009
http://dx.doi.org/10.1109/TCSVT.2009.2017402

[4] Alarcon-Aquino, V. Starostenko, O. Ramirez-Cortes, J.M., Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform, Engineering Intelligent Systems, vol. 17, pp. 49-63, March 2009

[5] Lexing Ying, Laurent Demanet, Emmanuel Candes, 3D discrete Curvelet transform.Applied and Computational Mathematics, USA:California Institute of Technology, 2005:38.

[6] Minhas, Rashid, Adeel Mohammed, Abdul, Jonathan Wu, Q.M., Shape from focus using fast discrete curvelet transform, Pattern Recognition, vol. 44, pp. 839-853, April 2011
http://dx.doi.org/10.1016/j.patcog.2010.10.015

[7] Brannock, Evelyn Weeks, Michael Harrison, Robert, The effect of wavelet families on watermarking, Journal of Computers, vol. 4, pp 554-566, June 2009
http://dx.doi.org/10.4304/jcp.4.6.554-566

[8] Huaqing, Zhou, Shenghui Dai, Wavelet descriptor for closed curves detection in complex background, Journal of Computers, vol. 5, pp. 1723-1730, 2010

[9] Y Lu, M.N Do, Multidimensional Directional FiIter Banks and Surfacelets, IEEE Transactions on Image Processing, vol. 16, pp 919-931, 2007.
http://dx.doi.org/10.1109/TIP.2007.891785
PMid:17405426

[10] Wang Yan, Rosen, David W. Multiscale heterogeneous modeling with surfacelets, Computer-Aided Design and Applications, vol. 7, pp. 759-776, 2010

[11] Xiao hongzhi, Yan jingwen, Qu xiaobo, A novel video denoising method with 3D context model Based on Surfacelet transform, chinese journal of electronics, vol. 36, pp 1460-1464,2008.

[12] Cao Jianian, Tang Chunrui, Liu, Dandan, An image denoising method combining surfacelet transform and multidirectional cycle spinning, Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, vol.30, pp. 952-956, August 2009

[13] Tang Chunrui, Liu Dandan, Cao Jianian, Denoising method for underwater acoustic transient signals based on surfacelet transform, Dalian Haishi Daxue Xuebao/Journal of Dalian Maritime University, vol. 34, pp 51-54, August 2008

[14] Liu changjin, Guo li. A Super-SAD matching criterion and its fast algorithm.Journal of circuits and systems, vol. 12, pp 137-141, 2007.

[15] Essannouni F, Thami, R. Oulad Haj, Aboutajdine D., Salam A, Adjustable SAD matching algorithm using frequency domain, Source: Journal of Real-Time Image Processing, vol. 1, pp. 257-265, July 2007
http://dx.doi.org/10.1007/s11554-007-0026-0

[16] Bruenig, Michael, Niehsen, Wolfgang, Fast full search block matching based on combined SAD and MSE measures, Proceedings of SPIE - The International Society for Optical Engineering, vol. 3653, pp. 439-449, 1999

[17] Xiang Youjun, Leina, YU Weiyu, Xie Sheng, Research of Block Matching Criterion for Motion Estimation, Computer science, vol. 36, pp. 278-280, 2009.

[18] Tao Yang, Liu Zhiming, Peng Yuxing, Ke Shuizhou, A New Matching Criterion and Block Matching Algorithm, Computer engineering & science, vol. 29, pp. 69-72, 2007.

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