An algorithm is proposed for processing and analyzing surface electromyography (SEMG) signals using wavelet transform and Higher Order Statistics (HOS). EMG signal acquires noise while travelling though different media. Wavelet denoising is performed in this research for initial EMG signal processing. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interferenc...

This paper presents a state-of-the-art adaptive wavelet-based denoising method with edge preservation. More specifically, a redundant discrete dyadic wavelet transform (DDWT) is performed on the noisy image to get the wavelet frame decomposition at different scales. Based on the Lipschitz regularity theory, correlation analysis across scales is performed to detect the significant coefficients f...

Objective The comparison between wavelet-based simultaneous multiscale denoising/hypothesis testing and single scale Gaussian spatial filtering followed by statistical testing of brain activation maps is carried out for a simple block-type visual paradigm EPI MRI experiment. Probabilistic wavelet shrinkage provides means to consistently combine multiresolution denoising and hypothesis testing i...

Transmitting the information in the form of images has drawn much importance in the modern age. The images are often corrupted by various types of noises during acquisition and transmission. Such images have to be cleaned before using in any applications. Image denoising is a thirst area in image processing for decades. Wavelet transform has been an efficient tool for image representation for d...

Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps – first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and fi...

Recently wavelet thresholding has been a popular approach to the 1-D and 2-D signal (image) denoising. In this work, instead of thresholding the wavelet coeecients, estimation approaches are proposed in the wavelet domain to reduce the noise. The fundamental philosophy is to consider the wavelet coeecients as a stationary random signal. Therefore, an optimal linear mean squared error estimate c...

We propose a new vein of feature vectors for robust speech recognition that use denoised wavelet coefficients. Greater robustness to unexpected additive noise or spectrum distortions begins with more robust acoustic features. The use of wavelet coefficients is motivated by human acoustic process modelling and by the ability of wavelet coefficients to capture important time and frequency feature...

The ECG signal is an important parameter for the diagnosis of heart disease. In the process of collection and transportation, ECG signals easily mixed with human body noise or the noise generated by the instrument. Therefore, the noise greatly affects the accuracy of the measurement. Wavelet threshold denoising method is widely used in denoising of ECG signal. Based on soft threshold and hard t...

We describe denoising one-dimensional signals by thresholding Blackman windowed Gabor transforms. This method is compared with Gauss-windowed Gabor threshold denoising and wavelet-based denoising, and is found to be superior in most cases. A new, localized estimator of noise standard deviation is also obtained. Our work provides the first step in developing an adaptive denoising method for non-...

This paper presents a comparative study of different wavelet denoising techniques and the results obtained were examined. The denoising process rejects noise by thresholding in the wavelet domain. It is observed that „rigrsure„ method gives optimum performance. Discrete wavelet transform has the benefit of giving a joint timefrequency representation of the signal. Also it is suitable for both s...