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Vibration signals are usually affected by noise, which is in turn related to the measurement and data processing procedures. This paper presents a new subband adaptive denoising method for detective impulsive signatures based on minimum description length principle with improved normalized maximum likelihood density model. The threshold of the proposed denoising method is determined automatically without the need to estimate the noise variance. The effectiveness of the proposed denoising method over VisuaShrink, BayesShrink and minimum description length denoising methods are given through simulation and practical applications.

This paper presents an image denoising algorithm based on image region analysis using a small hardware resource, which has low complexity compared with existing non-local denoising algorithms. The proposed method classifies image regions into three categories and performs adaptive denoising accordance to the characteristics of each category.

Lowering the cumulative radiation dose to a patient undergoing fluoroscopic examination requires efficient denoising algorithms. We propose a method, which extensively utilizes temporal dimension in order to maximize denoising efficiency. A set of subsequent images is processed and two estimates of denoised images are calculated. One is based on a special implementation of an adaptive edge preserving wavelet transform, while the other is based on the statistical method intersection of confidence intervals (ICI) rule. Wavelet transform is thought to produce high quality denoised images and ICI estimate can be used to further improve denoising performance about object edges. The estimates are fused to produce the final denoised image. We show that the proposed method performs very well and do not suffer from blurring in clinically important parts of images. As a result, its application could allow for significant lowering of the fluoroscope single frame dose.

This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and at high noise levels it provides improvements in both objective and subjective quality of the denoised image when compared to the state-of-the-art methods.

The lightning current derivative data recorded at the CN Tower during the past 18 years contain different kinds of noise and needs to be denoised for accurately determining the lightning current waveform parameters. It is usually a challenging task to denoise transient signals having large bandwidth without altering their waveshapes or shrinking their amplitudes. This paper deals with denoising the CN Tower lightning current derivative signals using several adaptive techniques. A new adaptive denoising approach (Divide-and-Conquer) has been successfully used to denoise a vast variety of CN Tower lightning current derivative waveshapes. The supremacy of the new technique over the existing ones is outlined for a signal with a poor signal-to-noise ratio (SNR). While keeping the signal amplitude unchanged and preserving its waveshape, the new denoising technique improved its SNR from -22.93 dB to 71.41 dB.

Chaos is a seemingly random and irregular movement, happening in a deterministic system without random factors. Chaotic theory has promising applications in various areas (e.g., communication, image encryption, geophysics, weak signal detection). However, observed chaotic signals are often contaminated by noise. The presence of noise hinders the chaos theory from being applied to related fields. Therefore, it is important to develop a new method of suppressing the noise of the chaotic signals. Recently, the denoising algorithm for chaotic signals based on collaborative filtering was proposed. Its denoising performance is better than those of the existing denoising algorithms for chaotic signals. The denoising algorithm for chaotic signals based on collaborative filtering makes full use of the self-similar structural feature of chaotic signals. However, in the parameter optimization issue of the denoising algorithm, the selection of the filter parameters is affected by signal characteristic, sampling frequency and noise level. In order to improve the adaptivity of the denoising algorithm, a criterion for selecting the optimal filter parameters is proposed based on permutation entropy in this paper. The permutation entropy can effectively measure the complexity of time series. It has been widely applied to physical, medical, engineering, and economic sciences. According to the difference among the permutation entropies of chaotic signals at different noise levels, first, different filter parameters are used for denoising noisy chaotic signals. Then, the permutation entropy of the reconstructed chaotic signal corresponding to each of filter parameters is computed. Finally, the permutation entropies of the reconstructed chaotic signals are compared with each other, and the filter parameter corresponding to the minimum permutation entropy is selected as an optimal filter parameter. The selections of the filter parameters are analyzed in the cases of different signal characteristics, different sampling frequencies and different noise levels. Simulation results show that this criterion can automatically optimize the filter parameter efficiently in different conditions, which improves the adaptivity of the denoising algorithm for chaotic signals based on collaborative filtering.

Vibration signal processing is commonly used in the mechanical fault diagnosis. It contains abundant working status information. The vibration signal has some features such as non-linear and non-stationary. It has a lot of interference information. Fault information is vulnerable to the impact of the interference information. Empirical mode decomposition denoising method and kurtosis correlation threshold have been widely used in the field of fault diagnosis. But the method mainly depends on the subjective experience, the large number of attempts, and lack of adaptability. In this article, the signals are decomposed into several intrinsic mode functions adaptively with ensemble empirical mode decomposition. The intrinsic mode functions containing the main fault information are selected by the correlation coefficient to emphasize the fault feature and inhibit the normal information. Finally, the energy features of these intrinsic mode functions are taken as inputs of a neural network to identify the fault patterns of rolling bearing. The experiment shows that the neural network diagnosis method based on ensemble empirical mode decomposition has a higher fault recognition rate than based on empirical mode decomposition or wavelet packet method.

In this paper, a new algorithm to denoise chaotic signals based on complete ensemble empirical mode decomposition (CEEMD-based) is proposed. In our scheme, the CEEMD technique is first used to decompose the noisy chaotic signal into the so-called intrinsic mode functions (IMFs). A novel criterion is proposed to determine which modes are used to reconstruct the denoised signal. In the experiments, we compare the proposed method with the algorithm based on empirical mode decomposition (EMD-based) and the algorithm based on ensemble empirical mode decomposition (EEMD-based). The experimental results show that the proposed method performs better than the EMD-based method and the EEMD-based method.

This paper proposes an adaptive denoising method that can significantly reduce Rician noise in magnetic resonance imag- ing (MRI). We use a Rayleigh kernel in the denoising pro- cessing of self-snakes instead of the Gaussian kernel that is usually used. The Rayleigh kernel is adaptively constructed according to the estimated standard deviation of Rician noise in images. The numerical implementation is carried out by applying the level-set techniques with a semi-implicit scheme. Experimental results in both synthetic and real im- ages demonstrate the effectiveness and advantages of the proposed method in comparison with the traditional methods.

We propose an adaptive denoising scheme for poorly modeled non-Gaussian features in the gravitational wave interferometric data. Preliminary tests on real data show encouraging results., Comment: 4 pages, 2 figures. Proceedings of GWDAW99 (Roma, Dec. 1999), to appear in Int. J. Mod. Phys. D

This paper describes a wavelet-transform-based system for the V wave identification in brainstem auditory evoked potentials (BAEP). The system combines signal denoising and rule-based localization modules. The signal denoising module has the potential of effective noise reduction after signal averaging. It analyses adaptively the evolution of the wavelet transform maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. A non-linear filtering process implemented with a neural network extracts out the noise-induced maxima. The filtered wavelet details are subsequently analysed by the rule-based localization module for the automatic identification of the V wave. In the first phase, it implements a set of statistical observations as well as heuristic criteria used by human experts in order to classify the IV–V complex. At the second phase, using a multiscale focusing algorithm, the IV and V waves are positioned on the BAEP signal. Our experiments revealed that the system provides accurate results even for signals exhibiting unclear IV–V complexes.

Utilizing the nonsubsampled contour let transform's(NSCT)advantages of translation invariance", "multi-resolution", "multidirection", "A image de-noising algorithm using adaptive bayes threshold by sub band based on nonsubsampled contour let transform is presented. Source images are decomposed to the domain of the NSCT, and According to different scales, different directions of sub-band energy, adaptive denoising threshold. Comparing with the multi-scale threshold using contour let transform and using adaptive threshold based on contour let transform, the simulation results show that the performance of this method is superior in Visual effects and psnr.

In the parameter optimization issue of nonlinear adaptive denoising algorithm for chaotic signals, the window length is affected by different factors. In this paper, a criterion is proposed for selecting the optimal window length. According to the difference in autocorrelation function between chaotic signal and noise, first, the different window sizes are used for denoising noisy chaotic signals. Then, the residual autocorrelation degree (RAD) of each window length is computed. Finally, the optimal window length is obtained by shrinking the window length corresponding to the minimum RAD. Simulation results show that this criterion can automatically optimize the window length efficiently under different conditions, which improves the adaptivity of the denoising algorithm of chaotic signals.

Localized or windowed data denoising based on linear transforms equipped with some thresholding operator is a usual approach in modern signal and image processing. With overlapping windows, techniques of this kind can be interpreted as overcomplete (redundant) data transforms (representations). In the simplest formulation, the final estimatesfor points belongingtomultiple overlapping windows are calculated as themean of the estimates independently obtained for each of the windows. In this paper we propose a general approach leading to a mix-distribution modeling of the overcomplete data and to least-squares optimal final estimates in the form of weighted average of the least-square estimates for the windowed data. Experiments demonstrate the advanced performance of this class of the algorithms, in particular in comparison with the standard ones using the sample averaging of the windowed estimates.