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Time-invariant orthonormal wavelet representations

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TLDR
This work addresses the time-invariance problem for orthonormal wavelet transforms and proposes an extension to wavelet packet decompositions to achieve time invariance and preserve the orthonormality.
Abstract
A simple construction of an orthonormal basis starting with a so-called mother wavelet, together with an efficient implementation gained the wavelet decomposition easy acceptance and generated a great research interest in its applications. An orthonormal basis may not, however, always be a suitable representation of a signal, particularly when time (or space) invariance is a required property. The conventional way around this problem is to use a redundant decomposition. We address the time-invariance problem for orthonormal wavelet transforms and propose an extension to wavelet packet decompositions. We show that it,is possible to achieve time invariance and preserve the orthonormality. We subsequently propose an efficient approach to obtain such a decomposition. We demonstrate the importance of our method by considering some application examples in signal reconstruction and time delay estimation.

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Citations
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Book

Wavelet Methods for Time Series Analysis

TL;DR: Wavelet analysis of finite energy signals and random variables and stochastic processes, analysis and synthesis of long memory processes, and the wavelet variance.
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Introduction to Wavelets and Wavelet Transforms: A Primer

TL;DR: This work describes the development of the Basic Multiresolution Wavelet System and some of its components, as well as some of the techniques used to design and implement these systems.
Book ChapterDOI

The Stationary Wavelet Transform and some Statistical Applications

TL;DR: In this article, two different approaches to the construction of an inverse of the stationary wavelet transform are described, and a method of local spectral density estimation is developed, which involves extensions to the wavelet context of standard time series ideas such as the periodogram and spectrum.
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A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery

TL;DR: A decomposition method based on the Douglas-Rachford algorithm for monotone operator-splitting for signal recovery problems and applications to non-Gaussian image denoising in a tight frame are demonstrated.
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Noise reduction using an undecimated discrete wavelet transform

TL;DR: A new nonlinear noise reduction method is presented that uses the discrete wavelet transform instead of the usual orthogonal one, resulting in a significantly improved noise reduction compared to the original wavelet based approach.
References
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Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI

Matching pursuits with time-frequency dictionaries

TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI

Orthonormal bases of compactly supported wavelets

TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI

Ideal spatial adaptation by wavelet shrinkage

TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.
Journal ArticleDOI

The wavelet transform, time-frequency localization and signal analysis

TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.