In this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager-Kaiser energy operator (TKEO) of signal IMFs. More precisely,...

Transmitting the information in the form of images has drawn much importance in the modern age. The images are often corrupted by various types of noises during acquisition and transmission. Such images have to be cleaned before using in any applications. Image denoising is a thirst area in image processing for decades. Wavelet transform has been an efficient tool for image representation for d...

We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operat...

Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present contribution lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps. These generalize Group LASSO and the previously introduced Elitist LASSO by introducing more flex...

The goal of denoising is to remove the noise while preserving the important features as much as possible. By exploring the power of parsimonious wavelet basis representation and statistical decision methods, Donoho and Johnstone [5] pioneered the wavelet shrinkage. However, the performance of traditional wavelet shrinkage is not even as good as that of a simple multiscale product method (MPM) [...

Data Denoising by Noise Invalidation c © Nima Nikvand, 2008 Master of Applied Science (MASc) Department of Electrical and Computer Engineering Ryerson University In this thesis, the problem of data denoising is studied, and two new denoising approaches are proposed. Using statistical properties of the additive noise, the methods provide adaptive data-dependent soft thresholding techniques to re...

We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the local and occupation times of Gaussian processes. The method relies on the almost-sure minimization of a Stein Unbiased Risk Estimator (SURE) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and...

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive w...

It is known that signals obtained from the real world environment are corrupted by the noise. This noise causes poor performance of the relevant system and therefore must be removed effectively before further processing of signal. Research in the area of wavelets showed that wavelet shrinkage method performs well and efficiently as compared to other methods of denoising. In this paper, a compar...

Global-local shrinkage hierarchies are an important, recent innovation in Bayesian estimation of regression models. In this paper we propose to use log-scale distributions as a basis for generating familes of flexible prior distributions for the local shrinkage hyperparameters within such hierarchies. An important property of the log-scale priors is that by varying the scale parameter one may v...