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Wavelet Video Denoising with Regularized Multiresolution Motion Estimation

Abstract

This paper develops a new approach to video denoising, in which motion estimation/compensation, temporal filtering, and spatial smoothing are all undertaken in the wavelet domain. The key to making this possible is the use of a shift-invariant, overcomplete wavelet transform, which allows motion between image frames to be manifested as an equivalent motion of coefficients in the wavelet domain. Our focus is on minimizing spatial blurring, restricting to temporal filtering when motion estimates are reliable, and spatially shrinking only insignificant coefficients when the motion is unreliable. Tests on standard video sequences show that our results yield comparable PSNR to the state of the art in the literature, but with considerably improved preservation of fine spatial details.

References

  1. 1.

    Brailean JC, Kleihorst RP, Efstratiadis S, Katsaggelos AK, Lagendijk RL: Noise reduction filters for dynamic image sequences: a review. Proceedings of IEEE 1995, 83(9):1272–1292. 10.1109/5.406412

    Article  Google Scholar 

  2. 2.

    Arce GR: Multistage order statistic filters for image sequence processing. IEEE Transactions on Signal Processing 1991, 39(5):1146–1163. 10.1109/78.80969

    Article  Google Scholar 

  3. 3.

    Kim J, Woods JW: Spatio-temporal adaptive 3-D Kalman filter for video. IEEE Transactions on Image Processing 1997, 6(3):414–424. 10.1109/83.557351

    Article  Google Scholar 

  4. 4.

    Chang SG, Yu B, Vetterli M: Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Transactions on Image Processing 2000, 9(9):1522–1531. 10.1109/83.862630

    MathSciNet  Article  Google Scholar 

  5. 5.

    Pizurica A, Zlokolica V, Philips W: Combined wavelet domain and temporal video denoising. Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '03), July 2003, Miami, Fla, USA 334–341.

    Google Scholar 

  6. 6.

    Ozkan MK, Sezan MI, Tekalp AM: Adaptive motion-compensated filtering of noisy image sequences. IEEE Transactions on Circuits and Systems for Video Technology 1993, 3(4):277–290. 10.1109/76.257217

    Article  Google Scholar 

  7. 7.

    Brailean JC, Katsaggelos AK: Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences. IEEE Transactions on Image Processing 1995, 4(9):1236–1251. 10.1109/83.413168

    Article  Google Scholar 

  8. 8.

    Kivanc Mihcak M, Kozintsev I, Ramchandran K, Moulin P: Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Processing Letters 1999, 6(12):300–303. 10.1109/97.803428

    Article  Google Scholar 

  9. 9.

    Pizurica A, Philips W, Lemahieu I, Acheroy M: A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising. IEEE Transactions on Image Processing 2002, 11(5):545–557. 10.1109/TIP.2002.1006401

    Article  Google Scholar 

  10. 10.

    Portilla J, Strela V, Wainwright MJ, Simoncelli EP: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing 2003, 12(11):1338–1351. 10.1109/TIP.2003.818640

    MathSciNet  Article  Google Scholar 

  11. 11.

    van Roosmalen PMB, Westen SJP, Lagendijk RL, Biemond J: Noise reduction for image sequences using an oriented pyramid thresholding technique. Proceedings of IEEE International Conference on Image Processing (ICIP '96), September 1996, Lausanne, Switzerland 1: 375–378.

    Article  Google Scholar 

  12. 12.

    Selesnick IW, Li KY: Video denoising using 2D and 3D dual-tree complex wavelet transforms. Wavelets: Applications in Signal and Image Processing X, August 2003, San Diego, Calif, USA, Proceedings of SPIE 5207: 607–618.

    Google Scholar 

  13. 13.

    Mallat S, Zhong S: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992, 14(7):710–732. 10.1109/34.142909

    Article  Google Scholar 

  14. 14.

    Rosenfeld A, Kak A: Digital Picture Processing. Acadamic Press, New York, NY, USA; 1982.

    Google Scholar 

  15. 15.

    Malfait M, Roose D: Wavelet-based image denoising using a Markov random field a priori model. IEEE Transactions on Image Processing 1997, 6(4):549–565. 10.1109/83.563320

    Article  Google Scholar 

  16. 16.

    Zhang Y-Q, Zafar S: Motion-compensated wavelet transform coding for color video compression. IEEE Transactions on Circuits and Systems for Video Technology 1992, 2(3):285–296. 10.1109/76.157160

    Article  Google Scholar 

  17. 17.

    Zan J, Ahmad MO, Swamy MNS: New techniques for multi-resolution motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2002, 12(9):793–802. 10.1109/TCSVT.2002.803225

    Article  Google Scholar 

  18. 18.

    Besag J: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B 1986, 48(3):259–302.

    MathSciNet  MATH  Google Scholar 

  19. 19.

    Liu J, Moulin P: Image denoising based on scale-space mixture modeling of wavelet coefficients. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 1: 386–390.

    Article  Google Scholar 

  20. 20.

    Pizurica A, Philips W, Lemahieu I, Acheroy M: A versatile wavelet domain noise filtration technique for medical imaging. IEEE Transactions on Medical Imaging 2003, 22(3):323–331. 10.1109/TMI.2003.809588

    Article  Google Scholar 

  21. 21.

    Donoho DL, Johnstone IM: Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994, 81(3):425–455. 10.1093/biomet/81.3.425

    MathSciNet  Article  Google Scholar 

  22. 22.

    Zlokolica V, Philips W: Motion- and detail-adaptive denoising of video. IS&T/SPIE 16th Annual Symposium on Electronic Imaging: Image Processing: Algorithms and Systems III, January 2004, San Jose, Calif, USA, Proceedings of SPIE 5298: 403–412.

    Google Scholar 

  23. 23.

    Bednar J, Watt T: Alpha-trimmed means and their relationship to median filters. IEEE Transactions on Acoustics, Speech, & Signal Processing 1984, 32(1):145–153. 10.1109/TASSP.1984.1164279

    Article  Google Scholar 

  24. 24.

    Cocchia F, Carrato S, Ramponi G: Design and real-time implementation of a 3-D rational filter for edge preserving smoothing. IEEE Transactions on Consumer Electronics 1997, 43(4):1291–1300. 10.1109/30.642398

    Article  Google Scholar 

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Correspondence to Fu Jin.

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Jin, F., Fieguth, P. & Winger, L. Wavelet Video Denoising with Regularized Multiresolution Motion Estimation. EURASIP J. Adv. Signal Process. 2006, 072705 (2006). https://doi.org/10.1155/ASP/2006/72705

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Keywords

  • Information Technology
  • Quantum Information
  • Video Sequence
  • Motion Estimate
  • Image Frame