Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Multihypothesis recursive video denoising based on separation of motion state

Multihypothesis recursive video denoising based on separation of motion state

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

  • Author(s): H. Tan 1, 2 ; F. Tian 1 ; Y. Qiu 1 ; S. Wang 1 ; J. Zhang 2
    • View affiliations
    • Affiliations: 1: The College of Communication Engineering, Chongqing University, Chongqing, People's Republic of China
      2: The College of Communication Engineering, The Chongqing Communication Institute, Chongqing, People's Republic of China
  • Source: Volume 4, Issue 4, August 2010, p. 261 – 268
    DOI:  10.1049/iet-ipr.2009.0279 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Published

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.

Inspec keywords: image motion analysis; image sequences; filtering theory; recursive filters; image denoising; video signal processing

Other keywords: additive Gaussian white noise; multihypothesis recursive video denoising filter; stationary motion; peak signal-to-noise ratio; nonstationary motion; local motion state; spatio-temporal varying filter; video sequence; joint filtering scheme; self-cross-bilateral filter; visual quality

Subjects: Filtering methods in signal processing; Optical, image and video signal processing; Video signal processing

References

    1. 1)
      • 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.
    2. 2)
      • L. Guo , O.C. Au , M. Ma , Z. Liang . Temporal video denoising based on multihypothesis motion compensation. IEEE Trans. Circuits Syst. Video Technol.
    3. 3)
      • 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
    4. 4)
      • M. Mahmoudi , G. Sapiro . Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett. , 839 - 842
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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
    9. 9)
      • A. Buades , B. Coll , J.M. Morel . Denoising image sequences does not require motion estimation.
    10. 10)
      • 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
    11. 11)
      • V. Zlokolica , A. Pizurica , W. Philips . Wavelet-domain video denoising based on reliability measures. IEEE Trans. Circuits Syst. Video Technol. , 993 - 1007
    12. 12)
      • 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.
    13. 13)
      • 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
    14. 14)
      • Z. Ming , B.K. Gunturk . Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. , 2324 - 2333
    15. 15)
      • 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.
    16. 16)
      • Tomasi, C., Manduchi, R.: `Bilateral filtering for gray and color images', Sixth Int. Conf. on Computer Vision, 1998, p. 839–846.
    17. 17)
      • E. Eisemann , F. Durand . Flash photography enhancement via intrinsic relighting. ACM Trans. Graph. (TOG) , 673 - 678
    18. 18)
      • Dugad, R., Ahuja, N.: `Video denoising by combining Kalman and Wiener estimates', Proc. Int. Conf. on Image Processing, ICIP, 1999, 4, p. 152–156.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2009.0279
Loading

Related content

content/journals/10.1049/iet-ipr.2009.0279
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address