Abstract
In this paper, a denoising algorithm for the Gaussian noise image using filtering-based estimation is presented. To adaptively deal with variety of the amount of noise corruption, the algorithm initially estimates the noise density from the degraded image. The standard deviation of the noise is computed from the different images between the noisy input and its’ pre-filtered version. In addition, the modified Gaussian noise removal filter based on the local statistics such as local weighted mean, local weighted activity and local maximum is flexibly used to control the degree of noise suppression. Experimental results show the superior performance of the proposed filter algorithm compared to the other standard algorithms in terms of both subjective and objective evaluations.
Chapter PDF
Similar content being viewed by others
References
Arce, G.R.: Nonlinear signal processing: A Statistical approach. John Wiley and Sons Inc. (2004)
Nodes, T.A., Gallagher, N.C.: Median filters: some modifications and their properties. IEEE Trans. Acoustics, Speech and Signal process. 30(5), 739–746 (1982)
Bednar, J.B., Watt, T.K.: Alpha-trimmed means and their relationship to median filter. IEEE Trans. Acoustics, Speech and Signal Process. 32(1), 145–153 (1984)
Olsen, S.I.: Noise Variance Estimation in Images: An evaluation, Computer Vision Graphics Image Processing. Graphic Models and Image Processing 55(4), 319–323 (1993)
Lee, J.S., Hoppel, K.: Noise modeling and estimation of remotely-sensed image. In: International Conference on Geoscience and Remote Sensing, Vancouver, Canada, vol. 2, pp. 1005–1008 (1989)
Shin, D.H., Park, R.H., Yang, S.J.: Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans. on Consumer Electronics 51(1) (2005)
Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. IEEE Proc. Vision Image Signal Process. 146, 8–84 (1999)
Lee, J.S.: Refined filtering of image noise using local statistics. Computer Vision, Graphics and Image processing 15, 380–389 (1989)
Mastin, G.A.: Adaptive filters for Digital noise smoothing, An evaluation. Computer vision, Graphics and Image processing 31, 103–121 (1985)
Crnojevic, V., Senk, V., Trpovski, Z.: Advanced impulse detection based on pixel-wise MAD. IEEE Signal Process. Letters 11(7), 589–592 (2004)
Aizenberg, I., Butakoff, C.: Effective impulse detector based on rank-order criteria. IEEE Signal Process. Letters 11(3), 363–366 (2004)
Zhang, X., Xiong, Y.: Impulse noise removal using directional differences based noise detector and adaptive weighted mean filter. IEEE Signal Process. Letters 16(4), 295–298 (2009)
Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10), 1141–1151 (2002)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, TA., Hong, MC. (2011). Filtering-Based Noise Estimation for Denoising the Image Degraded by Gaussian Noise. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25346-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-25346-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25345-4
Online ISBN: 978-3-642-25346-1
eBook Packages: Computer ScienceComputer Science (R0)