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In this paper, we propose readability enhancement of low light videos based on discrete wavelet transform (DWT). Captured videos under the low light condition have a narrow dynamic range (low contrast) with a dark tone as well as are highly corrupted by noise. We achieve both contrast enhancement and noise reduction in low light videos using wavelet coefficients. First, we perform normalization to...
In biomedical signal processing, Power Line Interference (50Hz) is one of the most and commonly types of electrical noises that often corrupt the quality of a biomedical data. In this paper, we present a simple tool for ECG signal enhancement approach based on Power Line Interference (PLI) reduction algorithm in Undecimated Wavelet Transform and Interval Thresholding. In our scheme, we use the Undecimated...
An adaptive data driven threshold is proposed for denoising one dimensional signals. The threshold is derived in a SURE (Stein Unbiased Risk Estimator) based framework using trimmed thresholding and the wavelet coefficients are obtained by a Translation Invariant transform. A detailed mathematical derivation of a hybrid scheme of the Universal threshold and the SURE threshold with trimmed thresholding...
Owing to its rapidly increasing popularity over last few decades the wavelet transform has become quite a standard tool in numerous research and application domains. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising because here multi-resolution analysis is possible. Wavelet thresholding is a signal...
Heart electrical activity is measured on the body surface; this measure is known as electrocardiogram (ECG). The ECG signals are commonly accompanied by different types of noise, that can lead to a difficult and imprecise computational process to diagnose heart diseases. In this paper, we propose the Kernel Principal Component Analysis (KPCA) method, usually used in image denoising, for minimizing...
An emerging issue in large-scale inverse problems is constituted by the interdependency between computational and recovery performance; in particular in practical application, such as medical imaging, it is crucial to provide high quality estimates given bounds on computational time. While most work in this direction has gone down the lines of improving optimisation schemes, in this paper we are proposing...
The paper is focused on finding appropriate and systematic procedures of denoising highly distorted small currents with significant “signal to noise” ratios and on evaluating the denoising effects over the power quality (PQ) indices. Real currents were acquired from 2 consumers placed in a small industrial hall with highly variable power consumptions. Denoising procedures relying on wavelet based...
We proposes a signal denoising framework algorithm which employs goodness of fit (GOF) test on complex wavelet coefficients obtained via dual tree complex wavelet transform (DT-CWT). Owing to its redundancy, DT-CWT is near translation invariant insuring better denoising performance over the classical discrete wavelet transform (DWT). The GOF test is used to identify the noisy DT-CWT coefficients whereby...
We here present a solution to solve a big data problem in the context of satellite image processing. We conceived a simulator for denoising Sentinel 1 SAR Single Look Complex images, by using block processing together with a method based on the Hurst parameter estimation in the wavelet domain. We compare our method with traditional denoising filters.
one of the factors, which affect efficiency, and life time of internal combustion engine is knock. Knock sensor is considered as a common sensor for detection of this phenomenon and it normally has low accuracy due to noise contamination of the knock signal. Nowadays, wavelet decomposition is used as a powerful method for noise reduction in signal processing. In this study, by employment of wavelet...
The paper is focused on finding appropriate procedures of denoising electrical signals (SN) acquired from the secondary winding of the excitation transformer in a large power group and evaluating their benefits. The signals were affected by white Gaussian noise with relatively high Signal to Noise ratios. The currents have significant harmonic contents (total harmonic distortions exceeding 25%). Two...
Computational techniques conceived to improve the efficiency of PQ analysis of quasi-stationary signals acquired from the secondary winding of the excitation transformer in a power plant are addressed. Signals of 30 periods (SN) were analyzed. SN were polluted by white Gaussian noise and "arithmetic average" signals (AS) of one period length were computed for 2 reasons: (a) simpler noise...
The possibilities of orthogonal wavelet functions for filtration of noisy signals used in the control systems are discussed in the present paper. Signals with high level noise/signal ratio are filtered. Different families of wavelet functions are used. The effectiveness of filtration by orthogonal and compactly supported wavelets is evaluated. The results are compared and analyzed.
Electrocardiogram (ECG) is a graphical recording of the electrical activity of human heart muscles. ECG is classified as a non-stationary signal. A major problem encountered with non-stationary signals is noise removal, particularly when the signal has a low signal-to-noise ratio (SNR). In this paper, the authors propose a hybrid method of β-hill climbing combined with wavelet transform for denoising...
In this work, we present an effective method for speckle noise reduction in digital speckle pattern interferometry (DSPI), which is based on a Riesz wavelet transform thresholding technique. Riesz wavelet transform is a steerable pyramid wavelet transform. Before Riesz-wavelet decomposition is applied to the noised image; the given coefficients undergo to thresholding technique, where appropriate...
Electrocardiography (Electrocardiogram — ECG) is the recording of electrical activity in the heart to examine the functioning of the heart muscle and neural transmission system. The graph obtained from this record is called electrocardiogram and the device used to record this graph is called electrocardiograph. In this study, a method based on second-order filter use was proposed to remove the white...
Low amplitude EEG signal are easily affected by various noise sources. This work presents de-noising methods based on the combination of stationary wavelet transform (SWT), universal threshold, statistical threshold and Discrete Wavelet Transform (DWT) with symlet, haar, coif, and bior4.4 wavelets. The results show significant improvement in performance parameter such as Signal to Artifacts ratio...
Vibration signal plays very important role in fault diagnosis of machine because it carries dynamic information of the machine. Signal processing is essentially needed to process and analyse signal but it is difficult to process and analyse the noisy signal. In many cases the noise signal is even stronger than the actual vibration signal, so it is important to have some mechanism in which noise elimination...
The aim of this paper is to present an efficient process of the denoising and the compression of the electrocardiogram signal (ECG). This process is based on an enhanced algorithm based on the discrete wavelet transform. Two different algorithms are proposed in this process. The first technique is a hybrid algorithm of the discrete wavelet transform and the adaptive dual threshold filter for the ECG...
Noise is an inevitable factor in an image. Several methods have been proposed to remove noise from an image. Of those wavelet transform based denoising is found to be remarkable since it works on different resolution levels. In this model different hybrid threshold have been proposed and experimented for Gaussian noise of different variance. These threshold algorithms are ranked based on their Signal...
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