Removal of Noise from Video Signals using Adaptive Temporal Averaging

This abstract proposed an algorithm for video denoising base on adaptive, pixel-wise, temporal averaging. This algorithm decomposes video signals into the set of 1-D time dependent signals and then removes the noise via establish the temporal averaging intervals during each signal from the set. Temporal averaging intervals established by simple, effective evaluation process which contain two-way thresholding. The proposed algorithm is experienced on quite a few types of 1-D signals and benchmark videos. Experiments advise that the proposed algorithm, regardless of its ease, produces high-quality denoising results.

[1]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[2]  Eero P. Simoncelli,et al.  Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain , 2002 .

[4]  Zhou Wang,et al.  Video denoising using a spatiotemporal statistical model of wavelet coefficients , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  M. Omair Ahmad,et al.  Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Aleksandra Pizurica,et al.  Recursive temporal denoising and motion estimation of video , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[7]  Ivan W. Selesnick,et al.  Video denoising using 2D and 3D dual-tree complex wavelet transforms , 2003, SPIE Optics + Photonics.