Noise Variance Estimation Based on Knowledge of Noise Reduction Coefficient

Article Preview

Abstract:

The paper presents interference estimation method involving search for “flat” area, on the basis of image analysis taking into account correlation coefficient and knowledge of variance reduction coefficient for noise in the filtered area. The proposed method was tested for an interference characterised by normal distribution, and then compared to the other ones.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

149-155

Citation:

Online since:

September 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] J.S. Lee, Refined Filtering of Image Noise Using Local Statistics Computer Vision, Graphics and Image Processing, vol. 15, 1981 pp, 380-389.

DOI: 10.1016/s0146-664x(81)80018-4

Google Scholar

[2] G.A. Mastin, Adaptive Filters for Digital Noise Smoothing, An Evaluation Computer Vision, Graphics and Image Processing, vol. 31, 1985 pp, 103-121.

DOI: 10.1016/s0734-189x(85)80078-5

Google Scholar

[3] P. Meer, J. Jolion, A. Rosenfeld, A Fast Parallel Algorithm for Blind Estimation of Noise Variance IEEE tr. on PAMI vol. 12, nr 2, 1990, pp.216-223.

DOI: 10.1109/34.44408

Google Scholar

[4] R. Bracho, A.C. Sanderson, Segmentation of Images Based on Intensity Gradient Information Proceedings of CVPR-85 Conf. On Computer Vision and Pattern Recognition, San Francisco, 1985, pp.341-347.

Google Scholar

[5] H. Vorhees, T. Poggio, Textons and Texture Boundaries in Natural Images Proceedings of 1 International Conf. On Computer Vision 1987, London, 1987, pp.250-258.

Google Scholar

[6] S.I. Olsen, Estimation of Noise in Images: An Evaluation Graph. Models Image Process. vol. 55, 1993, pp.319-323.

Google Scholar

[7] J. Purczyński, J. Ayman Comparison of Properties of Median Filter and Moving Average Filter Conf. Mat. - ZKwE'2000, pp.353-356, Poznan (2000).

Google Scholar

[8] F. Murtagh, J.L. Starc, Image Processing Through Multiscale Analysis and Measurement Noise Modelling Statistics and Computing Journal, vol. 10 pp.95-103, (2000).

Google Scholar

[9] M.R. Banham, A.K. Katsaggelos, Digital Image Restoration IEEE Signal Processing Magazine, vol. 3, pp.24-41, (1997).

DOI: 10.1109/79.581363

Google Scholar

[10] J. Canny, A Computational Approach to Edge Detection IEEE trans. on PAMI, vol. 9, pp.679-698, November (1986).

Google Scholar

[11] A. Amer, A. Mitiche, Reliable and Fast Structure-Oriented Video Noise Estimation Proceedings IEEE International Conference, ICIP 2002, vol. 1, pp.840-843, Rochester, USA, September (2002).

DOI: 10.1109/icip.2002.1038156

Google Scholar

[12] R.M. Henkelman, Measurement of Signal Intensities in the Presence of Noise in MR Images Medical Physics, vol. 2, no. 2, pp.232-233, 1085.

DOI: 10.1118/1.595711

Google Scholar