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Hermite Interpolation Based Interval Shannon-Cosine Wavelet and Its Application in Sparse Representation of Curve

Author

Listed:
  • Aiping Wang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Li Li

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Shuli Mei

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Kexin Meng

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Using the wavelet transform defined in the infinite domain to process the signal defined in finite interval, the wavelet transform coefficients at the boundary are usually very large. It will bring severe boundary effect, which reduces the calculation accuracy. The construction of interval wavelet is the most common method to reduce the boundary effect. By studying the properties of Shannon-Cosine interpolation wavelet, an improved version of the wavelet function is proposed, and the corresponding interval interpolation wavelet based on Hermite interpolation extension and variational principle is designed, which possesses almost all of the excellent properties such as interpolation, smoothness, compact support and normalization. Then, the multi-scale interpolation operator is constructed, which can be applied to select the sparse feature points and reconstruct signal based on these sparse points adaptively. To validate the effectiveness of the proposed method, we compare the proposed method with Shannon-Cosine interpolation wavelet method, Akima method, Bezier method and cubic spline method by taking infinitesimal derivable function c o s ( x ) and irregular piecewise function as an example. In the reconstruction of c o s ( x ) and piecewise function, the proposed method reduces the boundary effect at the endpoints. When the interpolation points are the same, the maximum error, average absolute error, mean square error and running time are 1.20 × 10 − 4 , 2.52 × 10 − 3 , 2.76 × 10 − 5 , 1.68 × 10 − 2 and 4.02 × 10 − 3 , 4.94 × 10 − 4 , 1.11 × 10 − 3 , 9.27 × 10 − 3 , respectively. The four indicators mentioned above are all lower than the other three methods. When reconstructing an infinitely derivable function, the curve reconstructed by our method is smoother, and it satisfies C 2 and G 2 continuity. Therefore, the proposed method can better realize the reconstruction of smooth curves, improve the reconstruction efficiency and provide new ideas to the curve reconstruction method.

Suggested Citation

  • Aiping Wang & Li Li & Shuli Mei & Kexin Meng, 2020. "Hermite Interpolation Based Interval Shannon-Cosine Wavelet and Its Application in Sparse Representation of Curve," Mathematics, MDPI, vol. 9(1), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:1-:d:466124
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    References listed on IDEAS

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    1. Carolyn E. Phelan & Daniele Marazzina & Gianluca Fusai & Guido Germano, 2019. "Hilbert transform, spectral filters and option pricing," Annals of Operations Research, Springer, vol. 282(1), pages 273-298, November.
    2. Shu-Li Mei & De-Hai Zhu, 2013. "Interval Shannon Wavelet Collocation Method for Fractional Fokker-Planck Equation," Advances in Mathematical Physics, Hindawi, vol. 2013, pages 1-12, December.
    3. Aràndiga, Francesc & Donat, Rosa & Romani, Lucia & Rossini, Milvia, 2020. "On the reconstruction of discontinuous functions using multiquadric RBF–WENO local interpolation techniques," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 176(C), pages 4-24.
    4. Ruyi Xing & Yanqiao Li & Qing Wang & Yangyang Wu & Shu-Li Mei, 2020. "Point-Symmetric Extension-Based Interval Shannon-Cosine Spectral Method for Fractional PDEs," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-10, June.
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