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The multi-scale wavelet transform algorithm is compared with median filter algorithm and morphological closing algorithm.
Hence, in this paper, an efficient VLSI (Very Large Scale Integrated Circuits) implementation of Trimmed median filter algorithm with T-Model mask technique is used for removal of impulse noise is proposed.
Then, the median filter algorithm is employed to pre-filter the white Gaussian noise from the measured data.
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Secondly, median filtering algorithm is used to reduce the "salt and pepper" noise after binarization.
First of all, in order to facilitate the application of SRG, median filtering algorithm and background correction algorithm [ 39] are applied on a given image, and then maximum Entropy Threshold [ 40] is applied to produce a binary image A. From the produced binary image A, Direction Cumulative Map H(A) is generated to find seed points.
In the presented paper we classify possible mistakes in time series and introduce median filtering algorithms for online and off-line "purification" of multiple time-series.
This article proposes an efficient median filter based algorithm to remove transient noise in a speech signal.
The traditional impulse noise filter algorithms, such as median filter and its modified versions, will cause the loss of dim targets and detail information along with the impulse noise.
Class merging transforms the five level output of the Otsu's algorithm, pre-filtered with a median filter that ensures discontinuities in the meniscus profile, to a binary image, in which the last four classes are combined.
This paper introduces a new switching-based median filtering scheme and algorithm for removal of impulse noise with reduced streaking under the constraint of reduced computational complexity.
For some modifications of our proposed algorithm, the suggested median filter post-processing reduced the average error by 34%.
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