Abstract
Least mean square (LMS) adaptive filters have been used in a wide range of one-dimensional signal processing applications. Recently adaptive filtering are presented that are based on the Euclidean Direction Search (EDS) method of optimization. The fast version of this class is called the Fast-EDS or FEDS algorithm. The FEDS based algorithms have a fast convergence rate and O(N) computational complexity. For two-dimensional image-processing applications there is two-dimensional least mean square (TDLMS) method. This paper discusses the results of applying a TDLMS, two dimensional normalized LMS and the new two dimensional Fast Euclidean Direction Search (TDF EDS) adaptive line enhancer for the Restoration of an image contaminated by noise. The results show that the TDFEDS algorithm can follow changes in image statistics and produces a very small amount of image distortion.
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© 2005 Springer-Verlag Berlin Heidelberg
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Abadi, M.S.E., Far, A.M., Ebrahimpour, R., Kabir, E. (2005). Image Restoration Using Two Dimensional Fast Euclidean Direction Search Based Adaptive Algorithm. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_26
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DOI: https://doi.org/10.1007/3-540-32391-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
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