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Adaptive digital image filtering in wavelet domain

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Eghbali H. J.

Machine Graphics and Vision

2003

tom Vol. 12, No. 2

279-290

In recent years wavelet transforms have been widely used for image denoising. This is because wavelet transform represents both the stationary and the transient behavior of the image. In this paper an adaptive filtering method is used for removing additive white Gaussian noise. It is based on statistics estimated from a local neighborhood of each wavelet coefficient. Denoising results compare favorably to the shrinkage denoising method, both perceptually and in terms of signal to noise ratio (SNR). The performance of the method is compared to shrinkage denoising method for both low and high (SNR) images.

Wpływ rozszerzenia próbki przy generowaniu współczynników falkowych szeregu na trafność prognozy

100%

Hadaś-Dyduch M.

Econometrics. Ekonometria. Advances in Applied Data Analytics

2014

nr 4(46)

62-71

The purpose of the study is to investigate the effect of the proposed method of sample extensions to the accuracy of the forecast series, in this case the WIG index. The forecast of series presenting the WIG was made on the basis of a model based on wavelet transform. Prior to the application of the extension number, the number of input data samples was divided into an even number of observations to define a more accurate forecasts. The article focuses only on the sample at an additional extension sets encapsulation of skipping the process of prediction coefficients. Therefore the article does not describe in detail the model used to predict because the aim of this article is not to evaluate the ability of prediction model and only the effect on the final outcome of prediction of six-rule extension method when determining the ak coefficients.

An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform

86%

Jenkal W. , Latif R. , Toumanari A. , Dliou A. , El B'charri O. , Maoulainine F. M. R.

Biocybernetics and Biomedical Engineering

2016

tom Vol. 36, no. 3

499--508

This paper proposes an efficient method of ECG signal denoising using the adaptive dual threshold filter (ADTF) and the discrete wavelet transform (DWT). The aim of this method is to bring together the advantages of these methods in order to improve the filtering of the ECG signal. The aim of the proposed method is to deal with the EMG noises, the power line interferences and the high frequency noises that could perturb the ECG signal. This algorithm is based on three steps of denoising, namely, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents certain applications of this algorithm on some of the MIT-BIH Arrhythmia database's signals. The results of these applications allow observing the high performance of the proposed method comparing to some other techniques recently published.