We present a novel signal de-noising algorithm for recovery of signals corrupted by a high levels of noise and applicable in the situations of low sampling rates. This method uses a modi cation of kernel partial least squares regression (KPLS) de ned in reproducing kernel Hilbert space [1]. We treat signal de-noising as a regression problem, in which de-noising consists of estimation of functio...

The paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in agricultural data collection system. As the there are influences of various factors in the collection process through wireless image sensor network, the detail signals of each scale are obtained from multi-scale analysis to replace the original signals with smooth low-frequency ...

Pulsed terahertz (T-ray) imaging systems represent an extremely promising method of obtaining sub-millimetre spectroscopic measurements for a wide range of applications. This paper investigates a number of techniques for optimally processing terahertz data. Speci cally we consider wavelet de-noising andWiener deconvolution algorithms. A goal of this research is the design and implementation of ...

We present a kernel based approach for image de-noising in the spatial domain. The crux of evaluation for the kernel weights is addressed by a Bayesian regression. This approach introduces an adaptive filter, well preserving edges and thin structures in the image. The hyper-parameters in the model as well as the predictive distribution functions are estimated through an efficient iterative sche...

Abstract: EEG signals are the versatile tool for detection of various kinds of Brain activities and diseases. But when the EEG data has been recorded for analysis purpose it is contaminated by different noise signals which are caused due to power line interference, electrode movement, base line wander, muscle movement (EMG) etc. and these days the E-health care system introduces in which there ...

--The most median-based de noising methods works fine for restoring the images corrupted by Random Valued Impulse Noise with low noise level but very poor with highly corrupted images. In this paper a directional weighted minimum deviation (DWMD) based filter has been proposed for removal of high random valued impulse noise (RVIN). The proposed approach based on Standard Deviation (SD) works in...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure does not require an estimate for the noise en...

Ultrasound elastography has been well applied in early tumor diagnosis for obtaining tissue stiffness information. Elastograpyy may provide useful clinical information for the tissue characterization. But ultrasonic wave interference will produce speckle in both phase and envelope. So in conventional ultrasound elastography, there are noise artifacts which produce some misdiagnosis. In this pap...

This paper proposes a spatially adaptive statistical model for wavelet image coefficients in order to perform image de-noising. The wavelet coefficients are modelled as zero-mean Gaussian random variables with high local correlation. This model is developed in a Bayesian framework, where a Maximum Likelihood (ML) estimator evaluates the variance of the blocks to which the wavelet subbands have ...