Multihypothesis recursive video denoising based on separation of motion state

Multihypothesis recursive video denoising based on separation of motion state

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A multihypothesis recursive video denoising filter (MRF) based on separation of motion state is proposed. For video sequence degraded by additive Gaussian white noise, local motion state will be detected combining multiple hypotheses (temporal predictions) first. Then different denoising schemes will be selected to suppress the noise according to the local motion state. Areas detected as stationary motion will be filtered by multihypothesis motion compensated filter (MHMCF), whereas areas detected as non-stationary motion will be filtered by self-cross-bilateral filter (SCBF). The definitions of stationary motion state and non-stationary motion state are given. In addition, the threshold used to classify motion state is equal to the noise standard deviation. The simulation results show that MRF outperforms conventional denoising methods like joint filtering scheme, spatio-temporal varying filter and MHMCF both in peak signal-to-noise ratio and visual quality.


    1. 1)
      • Zlokolica, V., Pizurica, A., Philips, W.: `Recursive temporal denoising and motion estimation of video', Int. Conf. on Image Processing, ICIP'04, 2004, 3, p. 1465–1468.
    2. 2)
      • V. Zlokolica , A. Pizurica , W. Philips . Wavelet-domain video denoising based on reliability measures. IEEE Trans. Circuits Syst. Video Technol. , 993 - 1007
    3. 3)
      • S.M.M. Rahman , M.O. Ahmad , M.N.S. Swamy . Video denoising based on inter-frame statistical modeling of wavelet coefficients. IEEE Trans. Circuits Syst. Video Technol.
    4. 4)
      • Varghese, G., Zhou, W.: `Video denoising using a spatiotemporal statistical model of wavelet coefficients', IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 2008, p. 1257–1260.
    5. 5)
      • J.C. Brailean , R.P. Kleihorst , S. Efstratiadis , A.K. Katsaggelos , R.L. Lagendijk . Noise reduction filters for dynamic image sequences: a review. Proc. IEEE , 1272 - 1292
    6. 6)
      • Li, Y., Yanfeng, Q.: `Novel adaptive temporal filter based on motion compensation for video noise reduction', Int. Symp. on Communications and Information Technologies, ISCIT'06, 2006, p. 1031–1034.
    7. 7)
      • Guo, L., Au, O.C., Mengyao, M., Zhiqin, L., Yuk, C.K.M.: `A multihypothesis motion-compensated temporal filter for video denoising', IEEE Int. Conf. on Image Processing, 2006, p. 1417–1420.
    8. 8)
      • L. Guo , O.C. Au , M. Ma , Z. Liang . Temporal video denoising based on multihypothesis motion compensation. IEEE Trans. Circuits Syst. Video Technol.
    9. 9)
      • L. Guo , O.C. Au , M. Ma , Z. Liang . Fast multi-hypothesis motion compensated filter for video denoising. IEEE Workshop on Signal Processing Systems , 283 - 288
    10. 10)
      • A. Buades , B. Coll , J.M. Morel . Denoising image sequences does not require motion estimation.
    11. 11)
      • M. Mahmoudi , G. Sapiro . Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett. , 839 - 842
    12. 12)
      • Dugad, R., Ahuja, N.: `Video denoising by combining Kalman and Wiener estimates', Proc. Int. Conf. on Image Processing, ICIP, 1999, 4, p. 152–156.
    13. 13)
      • Tai-Wai, C., Au, O.C., Tak-Song, C., Wing-San, C.: `A novel content-adaptive video denoising filter', Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP'05, 2005, 2, p. pp. ii/649–ii/652.
    14. 14)
      • D.T. Kuan , A.A. Sawchuk , T.C. Strand , P. Chavel . Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. , 165 - 177
    15. 15)
      • Tomasi, C., Manduchi, R.: `Bilateral filtering for gray and color images', Sixth Int. Conf. on Computer Vision, 1998, p. 839–846.
    16. 16)
      • E. Eisemann , F. Durand . Flash photography enhancement via intrinsic relighting. ACM Trans. Graph. (TOG) , 673 - 678
    17. 17)
      • G. Petschnigg , R. Szeliski , M. Agrawala , M. Cohen , H. Hoppe , K. Toyama . Digital photography with flash and no-flash image pairs. ACM Trans. Graph. (TOG) , 664 - 672
    18. 18)
      • Z. Ming , B.K. Gunturk . Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. , 2324 - 2333

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