Wavelets and digital filters designed and synthesized in the time and frequency domains

Viliam Ďuriš; Vladimir I. Semenov; Sergey G. Chumarov; Viliam Ďuriš; Vladimir I. Semenov; Sergey G. Chumarov

doi:10.3934/mbe.2022141

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 3056-3068. doi: 10.3934/mbe.2022141

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Research article

Wavelets and digital filters designed and synthesized in the time and frequency domains

1.
Department of Mathematics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, Nitra 94901, Slovakia
2.
Department of General Physics, I. N. Ulyanov Chuvash State University, Cheboksary 428015, Russia
3.
Department of Radio Engineering, I. N. Ulyanov Chuvash State University, Cheboksary 428015, Russia

Academic Editor: Yang Kuang

Received: 02 November 2021 Revised: 18 December 2021 Accepted: 28 December 2021 Published: 19 January 2022

The relevance of the problem under study is due to the fact that the comparison is made for wavelets constructed in the time and frequency domains. The wavelets constructed in the time domain include all discrete wavelets, as well as continuous wavelets based on derivatives of the Gaussian function. This article discusses the possibility of implementing algorithms for multiscale analysis of one-dimensional and two-dimensional signals with the above-mentioned wavelets and wavelets constructed in the frequency domain. In contrast to the discrete wavelet transform (Mallat algorithm), the authors propose a multiscale analysis of images with a multiplicity of less than two in the frequency domain, that is, the scale change factor is less than 2. Despite the fact that the multiplicity of the analysis is less than 2, the signal can be represented as successive approximations, as with the use of discrete wavelet transform. Reducing the multiplicity allows you to increase the depth of decomposition, thereby increasing the accuracy of signal analysis and synthesis. At the same time, the number of decomposition levels is an order of magnitude higher compared to traditional multi-scale analysis, which is achieved by progressive scanning of the image, that is, the image is processed not by rows and columns, but by progressive scanning as a whole. The use of the fast Fourier transform reduces the conversion time by four orders of magnitude compared to direct numerical integration, and due to this, the decomposition and reconstruction time does not increase compared to the time of multiscale analysis using discrete wavelets.
- wavelet transform,
- decomposition,
- reconstruction,
- multiscale analysis,
- frequency response,
- digital filter
Citation: Viliam Ďuriš, Vladimir I. Semenov, Sergey G. Chumarov. Wavelets and digital filters designed and synthesized in the time and frequency domains[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 3056-3068. doi: 10.3934/mbe.2022141

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Abstract

The relevance of the problem under study is due to the fact that the comparison is made for wavelets constructed in the time and frequency domains. The wavelets constructed in the time domain include all discrete wavelets, as well as continuous wavelets based on derivatives of the Gaussian function. This article discusses the possibility of implementing algorithms for multiscale analysis of one-dimensional and two-dimensional signals with the above-mentioned wavelets and wavelets constructed in the frequency domain. In contrast to the discrete wavelet transform (Mallat algorithm), the authors propose a multiscale analysis of images with a multiplicity of less than two in the frequency domain, that is, the scale change factor is less than 2. Despite the fact that the multiplicity of the analysis is less than 2, the signal can be represented as successive approximations, as with the use of discrete wavelet transform. Reducing the multiplicity allows you to increase the depth of decomposition, thereby increasing the accuracy of signal analysis and synthesis. At the same time, the number of decomposition levels is an order of magnitude higher compared to traditional multi-scale analysis, which is achieved by progressive scanning of the image, that is, the image is processed not by rows and columns, but by progressive scanning as a whole. The use of the fast Fourier transform reduces the conversion time by four orders of magnitude compared to direct numerical integration, and due to this, the decomposition and reconstruction time does not increase compared to the time of multiscale analysis using discrete wavelets.

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