Open AccessA study on optimally constructed compactly supported orthogonal wavelet filters
Author(s) -
Yujie Fan,
Qian Hu,
Yun Pan,
Chaosheng Huang,
Chao Chen,
KuanChing Li,
Weiguo Lin,
Xingang Wu,
Li. Yaxuan,
Wenqian Shang
Publication year - 2022
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis210410052f
Subject(s) - wavelet , nonlinear system , multiresolution analysis , computer science , filter (signal processing) , algorithm , mathematics , range (aeronautics) , orthogonal wavelet , scaling , gauss , mathematical optimization , wavelet transform , wavelet packet decomposition , artificial intelligence , geometry , physics , materials science , quantum mechanics , composite material , computer vision
Compactly supported orthogonal wavelet filters are extensively applied to the analysis and description of abrupt signals in fields such as multimedia. Based on the application of an elementary method for compactly supported orthogonal wavelet filters and the construction of a system of nonlinear equations for filter coefficients, we design compactly supported orthogonal wavelet filters, in which both the scaling and wavelet functions have many vanishing moments, by approximately solving the system of nonlinear equations. However, when solving such a system about filter coefficients of compactly supported wavelets, the most widely used method, the Newton Iteration method, cannot converge to the solution if the selected initial value is not near the exact solution. For such, we propose optimization algorithms for the Gauss-Newton type method that expand the selection range of initial values. The proposed method is optimal and promising when compared to other works, by analyzing the experimental results obtained in terms of accuracy, iteration times, solution speed, and complexity.