Improved video denoising method based on motion compensated sphere bilateral filtering

被引:0
|
作者
Tan H.-T. [1 ,2 ]
Tian F.-C. [1 ]
Zhang S. [1 ]
Zhang J. [2 ]
Qiu Y. [1 ]
机构
[1] Coll. of Communication Engineering, Chongqing Univ.
[2] Chongqing Communication Inst.
关键词
Bilateral filtering; Denoising; Motion compensation; Video processing;
D O I
10.3969/j.issn.1001-506X.2010.12.42
中图分类号
学科分类号
摘要
An improved video denoising method based on motion compensated sphere bilateral filtering (MCSBF) is proposed. For video sequence degraded by noise, the reference frames of the current frame are built first by motion compensation between the current frame and the past/future frames. Then in the three-dimensional space which is composed of the current frame and the compensated reference frames, a three-dimensional bilateral filter in which the filtering window is a sphere is used to suppress the noise in the current frame. By fully utilizing temporal and spatial correlations in video content, the proposed method can effectively suppress the noise in the video while keeps textures and details well. Simulation results show that MCSBF outperforms conventional denoising methods like spatial temporal joint filtering scheme (JNT), spatial temporal varying filter (STVF) and multi-hypothesis motion compensated filter (MHMCF) both in peak signal noise ratio (PSNR) and visual quality.
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页码:2701 / 2706
页数:5
相关论文
共 18 条
  • [1] Zlokolica V., Recursive temporal denoising and motion estimation of video, Proc. of International Conference on Image Processing, pp. 1465-1468, (2004)
  • [2] Zlokolica V., Pizurica A., Philips W., Wavelet-domain video denoising based on reliability measures, IEEE Trans. on Circuits and Systems for Video Technology, 16, 8, pp. 993-1007, (2006)
  • [3] Rahman S.M.M., Ahmad M.O., Swamy M.N.S., Video denoising based on inter-frame statistical modeling of wavelet coefficients, IEEE Trans. on Circuits and Systems for Video Technology, 17, 2, pp. 187-198, (2007)
  • [4] Varghese G., Video denoising using a spatiotemporal statistical model of wavelet coefficients, Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1257-1260, (2008)
  • [5] Brailean J.C., Noise reduction filters for dynamic image sequences: A review, Proc. of the IEEE, 83, 9, pp. 1272-1292, (1995)
  • [6] 28, 5, pp. 747-750, (2007)
  • [7] Guo L., Au O.C., Ma M., Et al., Temporal video denoising based on multihypothesis motion compensation, IEEE Trans. on Circuits and Systems for Video Technology, 17, 10, pp. 1423-1429, (2007)
  • [8] Patti A.J., Tekalp A.M., Sezan M.I., A new motion-compensated reduced-order model Kalman filter for space-varying restoration of progressive and interlaced video, IEEE Trans. on Image Processing, 7, 4, pp. 543-554, (1998)
  • [9] Buades A., A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60-65, (2005)
  • [10] Mahmoudi M., Sapiro G., Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal Processing Letters, 12, 12, pp. 839-842, (2005)