Spatio - Temporal Video Denoising by Block - Based Motion detection

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-8                      
Year of Publication : 2013
Authors : Seema Mishra , Preety D Swami

MLA 

Seema Mishra , Preety D Swami. "Spatio - Temporal Video Denoising by Block - Based Motion detection". International Journal of Engineering Trends and Technology (IJETT). V4(8):3371-3382 Jul 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

This paper proposes a new video denoising technique where spatially adaptive noise filtering in wavelet (transform) domain is combined with temporal filtering in signal domain. AWGN is being considered which behaves as Gaussian random variable. In this paper , spatial filtering of individual frames is done in the wa velet domain, and the filtering between the frames is done by recursive temporal filter. Spatial filtering is done by taking wavelet transform of individual frames and then modifying the wavelet coefficients by spatially adaptive bayesian wavelet shrinkage method. The denoising artifacts and residual noise differ from frame to frame which produces unpleasant visual effect. Hence filtering in time domain is essential. Temporal filte ring is based on a simple block based motion detector and on selective recursive time averaging of frames. This technique outperforms sequential spatio - temporal filters , 2 - D spatial filters and 3 - D (spatio - temporal) in terms of visual quality as well as quantitative (PSNR) performance measures .

References

[1]. P. Karunakaran, S.Venkatraman , I.Hameem Shanavas , and T.Kapilachander, “Denoising of noisy pixels in video by neighborhood correlation filtering a lgorithm,” I.J. Image, Graphics and Signal Proc. , vol. 4, no. 7, pp. 61 - 67, Jul. 2012.
[2]. V. Zlokolica, A. Pizur ica , and W. Philips, “Wavelet - domain video denoising based on reliability m easures,” IEEE Trans. on Circuits and Systems for Video T echnology , vol. 16, no. 8, pp. 993 - 1007, Aug . 2006.
[3]. J. C. Brailean, R. P. Kleihorst, S. Efstratidis, K. A Katsaggeleos, and R. L. Lagendijk, “Noise reduction filter s for dynamic image sequences: A review,” IEEE Trans. on Image Process. , vol. 83, no. 9, pp. 1272 – 1292, Sep. 1995.
[4]. D. L. Donoho, “De - noising by soft - thresholding,” IEEE Trans. Inform. Theor y , vol. 41, no. 3, pp. 613 – 627, May 2005.
[5]. E. P. Simoncelli and E. H. Adelson, “Noise removal via Bayesian wavelet coring,” in Proc. IEEE Int. Conf. Image Proc. , Lausanne , Switzerland , vol. 1, pp. 379 – 382 , Sep.1996.
[6]. V. Zlokolica , “Advanced nonlinear methods for video denoising”, Ph.D. Thesis, G h ent University, 2006.
[7]. A. Pizurica, V. Zlokolica, and W. Philips, “Combined wavelet domain and temporal video denoising,” in Proc. IEEE Conf. Adv. Video Signal - Based Surveillance , Miami , Fla , USA vol. 1, pp. 334 – 341 , Jul. 2003 .
[8]. R. Dugad and N. Ahuja, “Video denoising by combining Kalman and W iener estimates,” in Proc. IEEE Int. C onf. Image Process, Kobe, Japan , vol. 4, pp. 156 – 159 , Oct. 1999 .
[9]. I. Daubechies, Ten Lectures on Wavelets , Philadelphia: SIAM, 1992.
[10]. S. Mallat, A wavelet tour of signal processing, Academic Press, London, 1998.
[11]. M. Vetterli and J. Kovacevi ́c, Wavelets and Subband Coding , Prentice - Hall 1995.
[12]. K. Kannan , S. Arumuga Perumal, and K. Arulmozhi, “Optimal decomposition level of discrete, stationary and dual tree complex wavelet transform for pixel based fusion of multi - focused i mages, ” Serbian Journal of Electrical Engineering , vol. 7, no. 1, 81 - 93 , May 2010.
[13]. S.G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Proc . , vol. 9, no. 9, pp. 1522 – 1531 , Sep. 2000
[14]. D.L. Donoho and I.M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika , vol. 8 1 , no. 3 , pp. 425 – 455, Aug . 1994.
[15]. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, “A joint inter - and intrascale statistical model for wavelet based bayesian image denoising,” IEEE Trans. Image Proc . , vol. 11, no. 5, pp. 545 – 557, May 2002 .
[16]. A. Pizurica and W. Philips, “Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising,” IEEE Trans. Image Process., vol. 15, no. 3, pp. 654 – 665, Mar. 2006.
[17]. M.K. Mihcak, I. Kozintsev, K. Ramchandran, and P. Moulin, “Low - complexity image denoising based on statistical modeling of wavelet coefficients,” IEEE Signal Proc. Lett. , vol. 6, no. 12, pp. 300 – 303, Dec.1999.
[18]. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P . Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. , vol. 13, no. 4, pp. 600 – 612, Apr. 2004 .
[19]. G. Varghese and Zhou Wang, “Video d enoising b ase d on a s patiotemporal Gaussian scale mixture m odel,” IEEE Trans on Circuits and Systems for Video T echnology , vol. 20 , no. 7, Jul. 2010.

Keywords
Motion detection, Recursive temporal filtering , Spatial adaptive Bayesian shrinkage, Video denoising , Wavelet transform .