Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram  (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...

Work discussed in this paper is related to the denoising of a CT image before further using it. In order to attain the objective Dual Tree Discrete Wavelet Transform (DT-DWT) and wiener filter are being used to remove the noise that are either introduced in the image during capturing or injected into the image during transmission. The DT-DWT has an excellent performance in the denoising field a...

This paper introduces a new technique called adaptive wavelet thresholding and wavelet packet transform to denoised the image based on generalized Gaussian distribution.It chooses an adaptive threshold value which is level and subband dependent based on analyzing the subband coefficients.Experimental results, on different test images under different noise intensity conditions, shows proposed al...

Due to the disadvantage of large amounts of data computation and image quality degradation of classical reconstruction algorithm, a novel adaptive method of image reconstruction denoising based on compressive sensing is proposed. Firstly, the wavelet approximate coefficients and detail coefficients from the image noise are Gaussian distribution, and have different variances in different levels....

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMM’s) that concisely models the statistical dependen...

One of the most challenging tasks for which EMD could be useful is that of non-parametric signal denoising, an area in which wavelet thresholding has been the dominant technique for many years. In this paper, the major wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show, that although a direct application of this principle in the EM...

Soft wavelet shrinkage and total variation (TV) denoising are two frequently used techniques for denoising signals and images, while preserving their discontinuities. In this paper we show that – under specific circumstances – both methods are equivalent. First we prove that 1-D Haar wavelet shrinkage on a single scale is equivalent to a single step of TV diffusion or regularisation of two-pixe...

This paper presents a wavelet-based algorithm to attenuate spatially correlated noise represented by a fractional Brownian motion. It generalizes the usual independence assumption by making the spatial relationship depend not only on the variance, but also on a long-memory parameter associated with the decay of autocorrelations. Wiener filtering in the wavelet domain obtains estimates of gray l...

In this paper a PDE based hybrid method for image denoising is introduced. The method is a bi-stage filter with anisotropic diffusion followed by wavelet based bayesian shrinkage. Here efficient denoising is achieved by reducing the convergence time of anisotropic diffusion. As the convergence time decreases, image blurring can be restricted and will produce a better denoised image than anisotr...

This paper presents a non-linear filtering method based on the multiresolution analysis of the Discrete Wavelet Transform (DWT). The main idea is to use the time-frequency localization properties of the wavelet decomposition. The proposed algorithm is using an extra decomposition of the identified noise in order to reduce the correlation between the electrocardiogram (ECG) signal and noise. The...