Abstract
In this paper, we propose a new method using a modified sigma filter. The sigma filter among many algorithms is one of the simplest de-noising methods. The threshold of the sigma filter uses the estimated standard deviation of the noise by block-based noise estimation using the adaptive Gaussian filtering. In the proposed method, an input image is first decomposed into two components according to direction features. Then, two components are applied; HPF and LPF. By applying a conventional sigma filter separately on each of them, the output image is reconstructed from the filtered components. Comparative results from experiments show that the proposed algorithm achieves higher gains than the sigma filter and modified sigma filter, which are 2.6 dB PSNR on average and 0.5 dB PSNR, respectively. When relatively high levels of noise are added, the proposed algorithm shows better performance than the two conventional filters.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall, Englewood Cliffs (1990)
Gonzales, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Englewood Cliffs (2002)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, ITP (1999)
Lee, J.S.: Digital Image Smoothing and the Sigma Filter. Computer Vision, Graphics, and Image Processing 24(2), 255–269 (1983)
Bilcu, R.C., Vehvilainen, M.: A Modified Sigma Filter for Noise Reduction in Images. In: Proceedings of the 9th WSEAS Circuits, Systems, Communications and Computers multiconference, WSEAS/CSCC 2005, vol. 15 (July 2005)
Olsen, S.I.: Estimation of noise in images: An evaluation. Graphical Models and Image Process 55, 319–323 (1993)
Shin, D.-H., Park, R.-H., Yang, S., Jung, J.-H.: Block-Based Noise Estimation Using Adaptive Gaussian Filtering. IEEE Transactions on Consumer Electronics 51(1), 218–226 (2005)
Zheng, X., Kamata, S., Yu, L.: Face Recognition with Local Gradient Derivative Patterns. In: TEMCON 2010 – 2010 IEEE Region 10 Conference, pp. 667–670 (November 2010)
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
Lim, HY., Gu, MR., Kang, DS. (2011). Noise Reduction in Image Using Directional Modified Sigma Filter. In: Park, J.J., Yang, L.T., Lee, C. (eds) Future Information Technology. Communications in Computer and Information Science, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22333-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-22333-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22332-7
Online ISBN: 978-3-642-22333-4
eBook Packages: Computer ScienceComputer Science (R0)