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1 次元リフティングおよび 2 次元リフティング、ローカル多項式変換、ローラン多項式

リフティングでは、特定の性質をもつ完全再構成フィルター バンクを徐々に設計することができます。リフティングの詳細と例については、Lifting Method for Constructing Waveletsを参照してください。



filters2lpFilters to Laurent polynomials
liftingSchemeCreate lifting scheme for lifting wavelet transform
liftingStepCreate elementary lifting step
lwt1-D lifting wavelet transform
ilwtInverse 1-D lifting wavelet transform
laurentMatrixCreate Laurent matrix
laurentPolynomialCreate Laurent polynomial
liftfiltApply elementary lifting steps on filters
lwt22-D Lifting wavelet transform
ilwt2Inverse 2-D lifting wavelet transform
lwtcoefExtract or reconstruct 1-D LWT wavelet coefficients and orthogonal projections
lwtcoef2Extract 2-D LWT wavelet coefficients and orthogonal projections
wave2lpLaurent polynomials associated with wavelet
mlptMultiscale local 1-D polynomial transform
imlptInverse multiscale local 1-D polynomial transform
mlptreconReconstruct signal using inverse multiscale local 1-D polynomial transform
mlptdenoiseDenoise signal using multiscale local 1-D polynomial transform


  • Lifting Method for Constructing Wavelets

    Learn about constructing wavelets that do not depend on Fourier-based methods.

  • Smoothing Nonuniformly Sampled Data

    This example shows to smooth and denoise nonuniformly sampled data using the multiscale local polynomial transform (MLPT). The MLPT is a lifting scheme (Jansen, 2013) that shares many characteristics of the discrete wavelet transform and works with nonuniformly sampled data.