Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients

The paper proposes a joint probability density function to model the video wavelet coefficients of any two neighboring frames and then applies this statistical model for denoising. The parameter of the density function that measures the correlation between the wavelet coefficients of the two frames is used as an index for the motion. The joint density function is employed for spatial filtering of the noisy wavelet coefficients by developing a bivariate maximum a posteriori estimator. A recursive time averaging of the spatially filtered wavelet coefficients is adopted for further noise reduction. Simulation results on test video sequences show an improved performance both in terms of the peak signal-to-noise ratio and the perceptual quality compared to that of the other denoising algorithms

[1]  N. Kingsbury.  Image processing with complex wavelets , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  Marcel J. T. Reinders,et al.  VIDEO ENHANCEMENT AND RESTORATION , 2005 .

[3]  Paul W. Fieguth,et al.  Wavelet Video Denoising with Regularized Multiresolution Motion Estimation , 2006, EURASIP J. Adv. Signal Process..

[4]  Oscar C. Au,et al.  A novel content-adaptive video denoising filter , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[5]  Hye-Yeon Cheong,et al.  Adaptive spatio-temporal filtering for video denoising , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[6]  Livio Tenze,et al.  Design and real-time implementation of a low-cost noise reduction system for video applications , 2004, Signal Process..

[7]  Cocchia,et al.  Design And Real-time Implementation Of A 3-D Rational Filter For Edge Preserving Smoothing , 1997, 1997 International Conference on Consumer Electronics.

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

[9]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[10]  A. Murat Tekalp,et al.  Adaptive motion-compensated filtering of noisy image sequences , 1993, IEEE Trans. Circuits Syst. Video Technol..

[11]  Aleksandra Pizurica,et al.  Video Denoising using Multiple Class Averaging with Multiresolution , 2003, VLBV.

[12]  Pierre Moulin,et al.  Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients , 2001, IEEE Trans. Image Process..

[13]  A. Pižurica,et al.  Noise reduction in video sequences using wavelet-domain and temporal filtering , 2004, SPIE Optics East.

[14]  Hwan-Gue Cho,et al.  Extended-Regular Sequence for Automated Analysis of Microarray Images , 2006, EURASIP J. Adv. Signal Process..

[15]  Yuan F. Zheng,et al.  Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  D. Donoho,et al.  Translation-Invariant DeNoising , 1995 .

[17]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[18]  M. Kazubek,et al.  Wavelet domain image denoising by thresholding and Wiener filtering , 2003, IEEE Signal Processing Letters.

[19]  Anil C. Kokaram,et al.  Motion picture restoration - digital algorithms for artefact suppression in degraded motion picture film and video , 2001 .

[20]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[22]  Vladimir Zlokolica,et al.  A new non-linear filter for video processing. , 2002 .

[23]  K. R. Subramanian,et al.  Efficient wavelet-based image denoising algorithm , 2001 .

[24]  R.L. Ewing,et al.  Fast, feature-based wavelet shrinkage algorithm for image denoising , 2003, IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH37502).

[25]  Eero P. Simoncelli,et al.  On Advances in Statistical Modeling of Natural Images , 2004, Journal of Mathematical Imaging and Vision.

[26]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

[27]  Nasir M. Rajpoot,et al.  Adaptive wavelet restoration of noisy video sequences , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[28]  Alin Achim,et al.  SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling , 2003, IEEE Trans. Geosci. Remote. Sens..

[29]  A. Pižurica,et al.  Combined wavelet domain and temporal video denoising , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[30]  M. N. Shanmukha Swamy,et al.  Bayesian algorithm for video noise reduction in the wavelet domain , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[31]  Md. Kamrul Hasan,et al.  Wavelet-domain iterative center weighted median filter for image denoising , 2003, Signal Process..

[32]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[33]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[34]  Aggelos K. Katsaggelos,et al.  Noise reduction filters for dynamic image sequences: a review , 1995, Proc. IEEE.

[35]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[36]  Il Kyu Eom,et al.  Wavelet-based denoising with nearly arbitrarily shaped windows , 2004, IEEE Signal Process. Lett..

[37]  Reginald L. Lagendijk,et al.  Noise reduction of image sequences using motion compensation and signal decomposition , 1995, IEEE Trans. Image Process..

[38]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[39]  G. Scheffler,et al.  New concepts on denoising and sharpening of video signals , 2001, ICCE. International Conference on Consumer Electronics (IEEE Cat. No.01CH37182).

[40]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[41]  Reginald L. Lagendijk,et al.  Noise reduction for image sequences using an oriented pyramid thresholding technique , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[42]  J. W. Woods,et al.  Motion Compensated Spatiotemporal Kalman Filter , 1993 .

[43]  I. Selesnick,et al.  Bivariate shrinkage with local variance estimation , 2002, IEEE Signal Processing Letters.

[44]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[45]  W. B. Collis,et al.  An innovative approach for spatial video noise reduction using a wavelet based frequency decomposition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[46]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing, 2nd Edition , 1999 .

[47]  Aleksandra Pizurica,et al.  A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising , 2002, IEEE Trans. Image Process..