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 ...

Data pre-processing is challenging as it involves extensive manual effort and time, includes the process of noise removal referred as signal denoising or simply denoising. Noises present in signals are difficult to recover using the traditional methods. Now Wavelet Transform is used for denoising techniques. The implementation results reveal that the proposed technique achieves a state-of-the-a...

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and an illustrative example is given. The wavelet kernel is proposed to construct wavelet support vector machine (WSVM). The result of experiment shows that the denoising method based on WSVM...

This paper presents a novel wavelet-based denoising and compression statistical model for 3D hippocampus shapes. Shapes are encoded using spherical wavelets and the objective is to remove noisy coefficients while keeping significant shape information. To do so, we develop a non-linear wavelet shrinkage model based on a data-driven Bayesian framework. We threshold wavelet coefficients by locally...

The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as ...

This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed “hysteresis” thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters o...

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising schemes are extensions of WaveLab routines, using the symmetric orthogonal cubic spline wavelet basis. ...

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition lev...

The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography. Wavelet methods can smooth out noise while preserving signiicant features of images. Mallat et al. proposed a wavelet-based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, we explore the properties of wave...

There has been a lot of research work dedicated towards image denoising. However, with the wide spread of image usage in many fields of our lives, it becomes very important to develop new techniques for image denoising. The previous research in image denoising was based on two of the famous techniques in the image denoising named 2-D Dual-tree Complex Wavelet Transform (2D DTCWT) and 2-D Double...