In many image transmission and acquisition situations, the image may become cornlpted by additive noise. De-noising refers to the process of removing the noise while maintaining good visual quality. This problem has assumed major significance with the increase in image related communication that has accompanied the exponential growth of the intemet. Traditionally, image quality is measured in t...

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distri...

Gearbox is an important mechanical device to transmit power. In order to ensure the normal operation of gearbox under the condition of top load, high efficiency and high precision, it’s necessary to extract fault feature information using signal processing method and to further analyze and research gearbox fault. In the paper an improved de-noising method based on de-noising of singular value d...

Image de-noising is an important scheme that makes image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression good quality. This study assumed residual consisted of with edges produced by subtracting the original low-pass-filter-smoothed The Moran statistics were then used measure variation in sp...

A new approach to modified S-transform based non-stationary power signal de-noising is presented in this paper. Modified S-transform is employed in de-noising techniques to separate high frequency noise which is prevalent in practical signals like power disturbance signals from the low frequency undistorted signals. In this we have tried to bring out the advantage of the modified S-transform an...

Magnetic resonance images acquired with high temporal resolution often exhibit large noise artifacts, which arise from physiological sources as well as from the acquisition hardware. These artifacts can be detrimental to the quality and interpretation of the time-course data in functional MRI studies. A class of wavelet-domain de-noising algorithms estimates the underlying, noise-free signal by...

This paper proposes time-frequency analysis of EEG spectrum and wavelet analysis in EEG de-noising. In this paper, the basic idea is to use the characteristics of multi-scale multi-resolution, using four different thresholds to wipe off interference and noise after decomposition of the EEG signals. By analyzing the results, understanding the effects of four different methods, it comes to a conc...

Traditional thermal power units are continuously replaced by renewable energies, of which fluctuations and intermittence impose pressure on the frequency stability system. Electrolytic aluminum load (EAL) accounts for large amount local electric loads in some areas. The participation EAL control has huge application prospects. However, controller design is difficult due to measurement noise sys...

The s-transform is a variable window of STFT and extension of wavelet. This paper discussed the principle and method of Wavelet De-noising, reduced noise of pulse signal based on wavelet. It is shown that wavelet de-noising can eliminate most noise, and preserve effectively sudden change of signal. This paper analyzed and compared the effect of denoising of pulse signal in different ways all st...

There are various de-noising algorithms and optimization methods for different signal and noise characteristics. However, the signals used in real application may have deviations from the model. For example: signal and/or noise may not be stationary or a proper model for them may not be available. MCG (magnetocardiography) is an example signal, where conventional de-noising methods are not givi...