Image Sequence Denoising with Motion Estimation in Color Image Sequences


  • M. V. Sarode Department of Computer Science & Engineering, Jawaharlal Darda Institute of Engineering & Technology, India
  • P. R. Deshmukh Department of Computer Science & Engineering & IT, SIPN's College of Engineering, India
Volume: 1 | Issue: 6 | Pages: 139-143 | December 2011 |


In this paper, we investigate the denoising of image sequences i.e. video, corrupted with Gaussian noise and Impulse noise. In relation to single image denoising techniques, denoising of sequences aims to utilize the temporal dimension. This approach gives faster algorithms and better output quality. This paper focuses on the removal of different types of noise introduced in image sequences while transferring through network systems and video acquisition. The approach introduced consists of motion estimation, motion compensation, and filtering of image sequences. Most of the estimation approaches proposed deal mainly with monochrome video. The most usual way to apply them in color image sequences is to process each color channel separately. In this paper, we also propose a simple, accompanying method to extract the moving objects. Our experimental results on synthetic and natural images verify our arguments. The proposed algorithm’s performance is experimentally compared with a previous method, demonstrating comparable results.


video denoising, motion estimation, thresholding, segmentation, image sequence, PSNR


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M. Protter, M. Elad, “Image Sequence Denoising via Sparse and Redundant Representations”, IEEE Transactions on Image Processing, Vol. 18, No. 1, pp. 27-35, 2009 DOI:

D. S. Alexiadis, G. D. Sergiadis, “Estimation of Motions in Color Image Sequences Using Hypercomplex Fourier Transforms”, IEEE Transactions on Image Processing, Vol. 18, No. 1, pp. 168-187, 2009 DOI:

M. Elad, M. Aharon, “Image Denoising via Learned Dictionaries and Sparse Representation”, International Conference Computer Vision and Pattern Recognition, New York, 2006

M. Elad, M. Aharon, “Image Denoising via Sparse and Redundant Representation Over Learned Dictionaries”, IEEE Transactions on Image Processing, Vol. 15, No. 12, pp. 3736–3745, 2006 DOI:

A. Buades, B. Coll, J. M. Morel, “A Review of Image Denoising Algorithms, with a New One”, Multiscale Modeling & Simulation, Vol. 4, No. 2, pp. 490–530, 2005 DOI:

J. Portilla, V. Strela, M. J. Wainwright, E. P. Simoncelli, “Image Denoising Using Scale Mixtures of Gaussians in the Wavelet Domain”, IEEE Transactions on Image Processing, Vol. 12, No. 11, pp. 1338–1351, 2003 DOI:

V. Zlokolica, A. Pizurica, W. Philips, “Recursive Temporal Denoising and Motion Estimation of Video”, Int. Conf. Image Processing, Singapore, 2004

F. Jin, P. Fieguth, L. Winger, “Wavelet Video Denoising with Regularized Multiresolution Motion Estimation”, EURASIP Journal on Applied Signal Processing, Vol. 2, pp. 1–11, 2006 DOI:

R. Dugad, N. Ahuja, “Video Denoising by Combining Kalman and Wiener Estimates”, Proceeding of International. Conference on Image Processing, Japan, 1999

V. Zlokolica, A. Pizurica, W. Philips, “Wavelet-domain Video Denoising Based on Reliability Measures”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 16, No. 8, pp. 993–1007, 2006 DOI:

T. A. Ell, “Hypercomplex Spectral Transformations”, Ph.D. dissertation, Univ. Minessota, Minneapolis, 1992

S. J. Sangwine, “Fourier Transforms of Colour Images Using Quaternion or Hypercomplex Numbers”, Electronics Letters, Vol. 32, pp. 1979–1980, 1996 DOI:

S.-C. Pei, C.-M. Cheng, “A Novel Block Truncation Coding of Color Images by Using Quaternion-moment Preserving Principle”, IEEE Int. Symp. Circuits and Systems, vol. 2, pp. 684–687, Atlanta, 1996

T. A. Ell, S. J. Sangwine, “Hypercomplex Fourier Transforms of Color Images”, IEEE Transactions on Image Processing, Vol. 16, No. 1, pp. 22–35, 2007 DOI:

W. R. Hamilton, Elements of Quaternion, Second edition. New York: Longmans, Green & Co., 1901

R. C. Gonzalez, R. E. Woods, Digital Image Processing, Pearson Education, Second edition, Prentice Hall, 2005

R. Garnet, T. Huegerich, C. Chui, W. He, “A Universal Noise Removal Algorithm With an Impulse Detector”, IEEE Transactions on Image Processing, Vol. 14, No. 11, pp. 1747-1754, 2005 DOI:


How to Cite

M. V. Sarode and P. R. Deshmukh, “Image Sequence Denoising with Motion Estimation in Color Image Sequences”, Eng. Technol. Appl. Sci. Res., vol. 1, no. 6, pp. 139–143, Dec. 2011.


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