In this paper the Authors present a new approach for image contrast enhancement based on fuzzy geometrical procedure in which statistics and fuzzy entropy work for getting the purpose and translating the problem as distances among points in fuzzy hyper-cubes. Interesting results have been carried out and they can be considered encouraging after comparison with established techniques.
Recent works have shown that artifact removal in biomedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both DWT and ICA methods. In this study we show that if the two methods are jointly implemented, it is possible to improve the performances for the artifact rejection procedure. We discuss in detail the new method and we also show how this method provides advantages with respect to DWT of ICA procedure. Finally, we tested the new approach on real data.
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