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Noise Reduction in Video Sequences – The State of Art and the Technique for Motion Detection

by Reeja S.r, N. P. Kavya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 8
Year of Publication: 2012
Authors: Reeja S.r, N. P. Kavya
10.5120/9304-3526

Reeja S.r, N. P. Kavya . Noise Reduction in Video Sequences – The State of Art and the Technique for Motion Detection. International Journal of Computer Applications. 58, 8 ( November 2012), 31-36. DOI=10.5120/9304-3526

@article{ 10.5120/9304-3526,
author = { Reeja S.r, N. P. Kavya },
title = { Noise Reduction in Video Sequences – The State of Art and the Technique for Motion Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 8 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number8/9304-3526/ },
doi = { 10.5120/9304-3526 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:57.496407+05:30
%A Reeja S.r
%A N. P. Kavya
%T Noise Reduction in Video Sequences – The State of Art and the Technique for Motion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 8
%P 31-36
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper provides a detailed state of the art of different video denoising techniques. Most of the video denoising algorithms are done through the motion detection technique. The main goal is to give a survey of various noise reduction techniques for video. Object detection is the first level of video denoising. The first level can be achieved through Motion Detection. This paper explained about the motion estimation and compensation techniques. The different video denoising techniques, motion detection techniques and the noises used are shown through the taxonomy.

References
  1. Malfait M, Roose D, Wavelet Based Image Denoising Using a Markov Random Field a Priori Model, IEEE Transaction on Image Processing, Vol. 6, No. 4, (1997) 549-565.
  2. Kazubek M,Wavelet Domain Image Denoising by Thresholding and Wiener Filtering, IEEE Signal Processing, Vol. 10, No. 11, (2003) 324-326.
  3. Van De Ville D, Van der Weken D ,Nachtegael M, Kerre E. E. , Philips W. , and Lemahieu I, Noise Reduction by Fuzzy Image Filtering, IEEE Transaction on Fuzzy Systems, Vol. 11, No. 4, (2003) 429-436.
  4. Sadhar S. I. , and Rajagopalan A. N, Image Estimation in Film-Grain Noise, IEEE Signal Processing Letters, Vol. 12, No. 3, (2005) 238-241.
  5. Ozkan M. K. , Sezan I. , and Tekalp A. M, Adaptive Motion Compensated Filtering of Noisy Image Sequences, IEEE Transaction on Circuits and Systems for Video Technology, Vol. 3, No. 4, (1993) 277-290.
  6. Dugad R. , and Ajuha N. , "Video Denoising by Combining Kalman and Wiener Estimates", IEEE International Conference on Image Processing, Kobe, Japan, 4, (1999) 152-161.
  7. Zlokolica V. , Philips W. , Van De Ville D, A New Non-Linear Filter for Video Processing, 3rd IEEE Benelux SPS-2002, Leuven, Belgium, (2002) 13-16.
  8. Selesnick I. W. , and Li K. Y, "Video Denoising Using 2d and 3d Dual-Tree Complex Wavelet Transforms", In Wavelet Applications in Signal and Image Processing (SPIE 5207), San Diego, 5207, (2003) 607-618.
  9. Pizurica A, Zlokolica V, Philips W, Combined Wavelet Domain and Temporal Video Denoising, IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS2003), (2003) page 334-341.
  10. Gupta N, Swamy M. N. S, and Plotkin E. I, Low-Complexity Video Noise Reduction in Wavelet Domain, IEEE 6th Workshop on Multimedia Signal Processing, (2004) 239-242.
  11. Chan T. -W, Au O. C, Chong T. S, and Chau W. S,A Novel Content-Adaptive Video Denoising Filter, IEEE ICASSP, Philadelphia, PA, USA, Vol. 2, (2005) 649-652.
  12. Nai-Xiang Lian, Vitali Zagorodnov and Yap Peng Tan, "Video denoising using vector estimation of wavelet coefficients", IEEE 2006, ISCAS2006.
  13. M Kermal Gullu, Oguzhan urhan and Sarp Eeturk, "Pixel domain spatio-temporal denoising for archive videos", ISCIS 2006, springer-Verlag Berlin Heidelberg 2006
  14. Vladimir Zlokolica, Aleksandra Pizurica, Wilfried Philips, Noise estimation for video processing based on saptio temporal gradients, IEEE signal processing letters,Vol. 13, No. 6, june 2006.
  15. Yue Lu And Minh N. Do, "video processing using 3D surfacelet transform", Urbana IL 61801,USA, 2006.
  16. Kostadin Dabov, Alessandro Foi and Karen Egiazarian, "video denoising by sparse 3D transform-domain collaborative filtering", EUSIPCO 2007, Poland, Sep. 3-7,2007.
  17. Hae Jong Seo and Peyman Milanfar, "video denoising using higher order optimal space-time adaptation", ICASSP 2008,I EEE 2008.
  18. Matan Protter,Michael Elad, Hiroyuki Takeda, and Peyman Milanfar, "Generalizing the nonlocal- means to super resolution Reconstructio", IEEE transaction on image processing, Volume18, Number1,Jan 2009.
  19. JulienMairal, GuillermoSapiro, MichaelElad, Learning multiscale sparse representation for image and video restoration, IMA Preprint Series of 2168.
  20. Rasha Orhan Mahmoud and Mahmoud T. Faheem, Comparison between DWT and Dual Tree Complex wavelet transform in video sequences using wavelet domain, infos2008, march27-29, eario-Egypt 2008.
  21. Jingyu Yang, Yao Wang, Wenli Xu and Qionghai Dai, "Image and video denoising using adaptive dual tree Discrete wavelet packets", IEEE transaction on circuits and systems for video technology.
  22. QuingXiongYang, Kar-Itan-Tan and Naredra Ahuja, Real time O(1) Bilateral filtering, IEEE 2009.
  23. Mahmoud Ghoniem, Yoursef chahir and Ahdrrahim Elmoatar," Non local video denoising, simplification and inpainting using discrete regularization on graphs", 2009 ELSEVIER.
  24. HuiJi,ChaoqiangLiu, Zuoweishon and Yuhongxu,"Robust video denoising using lowranKmatrixcompletion,http. //media. xiph. org/video/derf.
  25. Ljubomir Jovanov, Aleksandra Pizurica, Stefan schulta,Peter Schelkon,Adrian munteanu, Etiennakerre and wlfried Philips," Combined wavelet domain and motion compensated video denoiding based on video codec motion estimation method", IEEE , Vol. 19,No. 3, March 2009.
  26. Roberto Di Salvo, Carmelo Pino. , Image and video processing on CUDA: state of the Art and Future Directions, ISBN-978-1-61804-046-6.
  27. Florian Luisier, Thierry Blue, Michael Unser, "surelet for orthonormal wavelet domain video denoising", 2010 IEEE ,Vol. 20, No. 6, JUNE 2010.
  28. William T. Freeman, C Liu, "A High quality video denoising algorithm based on reliable motion estimation", 2010 Spinger, ECCV2010.
  29. Radha s. Shirbhat, Nitish D. Mishra and Rasika P. Pande, "video survilence system using motion detection A Survey", IJANA,Vol. 3,I-5,P-(19-22),2012.
  30. G. Healey, R. Kondepudy, Radiometric ccd camera calibration and noise estimation, IEEE, PAMI, 267– 276, 1994.
  31. Jingjng Dai, Oscar C Au, Chao Pang, feng Zou, Combined inter frame and inter color prediction for color video denoising, 2012 IEEE international conference on multimedia and Expo
  32. Michal Joachimiak, Dmytro Rusanovsky, Miska, Moncef, "Multiview 3D video denoising in sliding 3D DCT Domain", EURASIP,Aug 27-31, 2012
  33. Yubing Han, Rushan Chen, "Efficient video denoising based on dynamic nonlocal means", Elsevier,Image and vision computing b30(2012) 78-85
  35. B. Dolwithayakul, C. chantrapornchai, N. Chumchob, Real time parallel spatio temporal video denoising scheme on Multicore processor, IEEE 2012
  36. Xiaolin Tian, LichengJiao, Ying Duan, "Video denoising via spatially adaptive coefficient shrinkage and threshold adjustment in surfacelet transform domain", Springer,15 may 2012
  37. MatteoMaggioni, GeacomoBoracchi, AlessandroFoi, KarenEgiazarian, Video denoising, Deblocking and enhancement through separable 4D nonlocal spatio temporal transform, IEEE Transaction on image processing, Volume21, Number9, Sep 2012
  38. JingjngDai, OscarCAu, ChaoPang fengZou, Combined inter frame and inter color prediction for color video denoising, IEEE 2011
  39. Lianghai Jin, Hang Liu, Xiangyang, Enmin Sony, "Quarternian based impulse noise removal from color video sequences", IEEE,2011
  40. Hossein Rabbani, Video deblurring in complex wavelet domain using local laplaceprior for enhancement and anisotropic spatially adaptive denoising for PSF detection, IEEE 2010
  41. YiWan, QiqiangChen, A Novel quadratic type variational method for efficient salt and pepper noise removal, IEEE 2010
  42. JingjngDai, OscarCAu, ChaoPang, fengZou, XingWen, Color Video denoising based on adaptive color space conversion, IEEE 2010
  43. H. Tan,F. Tian, Y. Qiu, S. Wang, J. Zbang, "Multihypothesis recursive video denoising based on separation of motion state", IET image process,2010,vol. 4,Iss. 4,pp. 261-268
  44. HijeshVarghese, ZhouWang, Video denoising based on spatio temporal guassian scale mixture model, IEEE Transaction on circuits and system for video technology, volume20, Number7, July2010
  45. LeiZhang, WeishengDong, Xiaolin Wu, GuangmingShi, Spatiotemporal color video reconstruction from noise CFA sequenc, IEEE Transaction on circuits and system for video technology, volume20, Number6, June2010
  46. ShgongYu, M. Omair, Ahmad, M. N. S. Swamy,Video denoising using motion compensated 3D wavelet transform with integrated recursive temporal filtering, On circuits and system for video technology, volume20, Number6, June2010
  47. K. Aisarya, v Jayaraj, D. Ebenezer, A New and efficient algorithm for the removal of high density salt and pepper noise in image and video, IEEE computer society, 2010
  48. Zhang, JangWooHan, JunHyungKin, sungJerko, A gradient salient based spatio temporal video noise reduction method for digital TV, IEEE. 2010
  49. KaiZeng, Zhouwang, polyview fusion: a strategy to enhance video denoising algorithm, IEEE. 2010
Index Terms

Computer Science
Information Sciences

Keywords

Object tracking frame differencing image and video denoising

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