Abstract
In this paper a new method is proposed for image denoising based on HMM (hidden Markov modeling). In this manner the noisy imageis modeled as a hidden Markov process, we use the local statistics of the images in defining the HMM parameters, and the image restoration is achieved by computing the most likely state at each pixel. Among the features of the proposed method is the adaptive window size for different regions of the image (smooth and nonsmooth regions). The adaptive window size allows us to obtain a better estimate of the local variance of the noise, therefore, the additive noise is removed more in the smooth regions while the edges are preserved in nonsmooth ones. Another feature of this method has to do with the proportionality of the execution time and the noise power; the less the noise power, the faster is the execution of the proposed algorithm. The performance of this algorithm is evaluated through subjective and objective criteria and it is shown that the restored images by HMM have higher contrast and clearness which is attributed to nearly optimal usage of the statistical properties of the image by HMM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Rosenfeld and A. Kak, Digital Picture Processing. New York: Academic, 1982.
P. K. Sinha and Q. H. Hong, “An improved median filter,” IEEE. Trans. Med.Image., vol. 9, pp. 345–346, Sept. 1990.
E.L. Hall, Computer Image Processing and Recognition. New York: Academic, 1979.
A. K. Jain, “Fundamentals of digital image processing”, Prentice-Hall, 1989.
A. Beghdadi and A. Khellaf, “A noise filtering method using the local information measure ”, IEEE Trans. On image image processing, Vol. 6, No. 6, June 1997.
L. Rabiner, B. H. Juang, “Fundamentals of speech recognition,” Prentice-Hall, Englewood Cliff, NJ, 1993.
S. J. Ko and Y. H. Lee, “Center Weighted Median filters and their application to image enhancement”, IEEE Trans. Circuits Syst., Vol. 38, 984–993, 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghabeli, L., Amindavar, H. (2002). Image Denoising Using Hidden Markov Models. In: Shafazand, H., Tjoa, A.M. (eds) EurAsia-ICT 2002: Information and Communication Technology. EurAsia-ICT 2002. Lecture Notes in Computer Science, vol 2510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36087-5_47
Download citation
DOI: https://doi.org/10.1007/3-540-36087-5_47
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00028-0
Online ISBN: 978-3-540-36087-2
eBook Packages: Springer Book Archive