2005Unpublished venueRequires access

Continuous wavelet transforms

Yunhui Shi, Qiuqi Ruan

Open publisher page 21 citations

Abstract

In this paper, we propose a new type of continuous wavelet transform. However we discretize the variables of integral a and b, any numerical integral has a high resolution and a does not appear in the denominator of the integrand. Furthermore, we give two discretization methods of the new wavelet transform. For the one-dimensional situation, we give quadrature formula of the discretized inverse wavelet transform. For the multidimensional situation, we develop the commonly wavelet network based on the discretized inverse wavelet transform of the new wavelet transform. Finally, the numerical examples show that the continuous wavelet transform constructed in this paper has higher computing accuracy compared with the classical continuous wavelet transform.

About this research paper

What this paper is about

In this paper, we propose a new type of continuous wavelet transform. However we discretize the variables of integral a and b, any numerical integral has a high resolution and a does not appear in the denominator of the integrand. Furthermore, we give two discretization methods of the new wavelet transform. For the one-dimensional situation, we give quadrature formula of the discretized inverse wavelet transform. For the multidimensional situation, we develop the commonly wavelet network based on the discretized inverse wavelet transform of the new wavelet transform. Finally, the numerical examples show that the continuous wavelet transform constructed in this paper has higher computing accuracy compared with the classical continuous wavelet transform.

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OpenAlex reports 21 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, we propose a new type of continuous wavelet transform. However we discretize the variables of integral a and b, any numerical integral has a high resolution and a does not appear in the denominator of the integrand. Furthermore, we give two discretization methods of the new wavelet transform. For the one-dimensional situation, we give quadrature formula of the discretized inverse wavelet transform. For the multidimensional situation, we develop the commonly wavelet network based on the discretized inverse wavelet transform of the new wavelet transform. Finally, the numerical examples show that the continuous wavelet transform constructed in this paper has higher computing accuracy compared with the classical continuous wavelet transform.

Key concepts: Discrete wavelet transform, Stationary wavelet transform, Second-generation wavelet transform, Wavelet transform, Wavelet packet decomposition, Harmonic wavelet transform, Wavelet, Lifting scheme

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