The de-noising is a challenging task in the field of signal and image processing. Any natural image corrupted by gussian noise can be de-noised using wavelet method. Wavelet-based image denoising is an important technique in the area of image noise reduction. Wavelets have their natural ability to represent images in a very sparse form which is the foundation of wavelet-based denoising through ...

In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

The natural frequency and scanning vibration frequency of C-dynamic scanning system of laser sensors are acquired for film thickness of lithium battery de-noising based on multi-resolution wavelet algorithm. For this reason, fast de-noising based on atomic sequence template library is present. First, under various mode of scanning, best atomic sequence template is built by sparse decomposition....

The wavelet de-noising technique with wavelet based function has been used in this paper for bearing fault detection. The applications of the wavelet de-noising show that the fault pulses in time-domain of the de-noised signals are easily to be detected as a result of removing the covering noise, which is not possible through the time-domain analysis of the original signal. Furthermore, the rec...

Donoho and Johnstone (1992a) proposed a method for reconstructing an unknown function f on [0; 1] from noisy data di = f(ti) + zi, i = 0; : : : ; n 1, ti = i=n, zi iid N(0; 1). The reconstruction f̂ n is de ned in the wavelet domain by translating all the empirical wavelet coe cients of d towards 0 by an amount p 2 log(n) = p n. We prove two results about that estimator. [Smooth]: With high prob...

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...

As many traditional de-noising methods fail in the intensive noises environment and are unadaptable in various noisy environments, a method of speech enhancement has been advanced based on dynamic Fractional Fourier Transform (FRFT)filtering. The acoustic signals are framed. The renewing methods are put in FRFT optimal disperse degree of noising speech and this method is implemented in detail. ...

SUMMARY We consider two score tests for heteroscedasticity in the errors of a signal plus noise model, where the signal is estimated by wavelet thresholding methods. The error variances are assumed to depend on observed covariates, through a parametric relationship of known form. The tests are based on the approaches of Breusch & Pagan (1979) and Koenker (1981). We establish the asymptotic vali...

The Monte Carlo dose calculation method works by simulating individual energetic photons or electrons as they traverse a digital representation of the patient anatomy. However, Monte Carlo results fluctuate until a large number of particles are simulated. We propose wavelet threshold de-noising as a postprocessing step to accelerate convergence of Monte Carlo dose calculations. A sampled rough ...

Abstract—Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise le...