Lifting

1-D and 2-D lifting, Local polynomial transforms, Laurent polynomials

Lifting allows you to progressively design perfect reconstruction filter banks with specific properties. For lifting information and an example, see Lifting Method for Constructing Wavelets.

Functions

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Lifting

`filters2lp`	Filters to Laurent polynomials
`liftingScheme`	Create lifting scheme for lifting wavelet transform
`liftingStep`	Create elementary lifting step
`lwt`	1-D lifting wavelet transform
`ilwt`	Inverse 1-D lifting wavelet transform
`laurentMatrix`	Create Laurent matrix
`laurentPolynomial`	Create Laurent polynomial
`liftfilt`	Apply elementary lifting steps on filters
`lwt2`	2-D Lifting wavelet transform
`ilwt2`	Inverse 2-D lifting wavelet transform
`lwtcoef`	Extract or reconstruct 1-D LWT wavelet coefficients and orthogonal projections
`lwtcoef2`	Extract 2-D LWT wavelet coefficients and orthogonal projections
`wave2lp`	Laurent polynomials associated with wavelet

Local Polynomial Transforms

`mlpt`	Multiscale local 1-D polynomial transform
`imlpt`	Inverse multiscale local 1-D polynomial transform
`mlptrecon`	Reconstruct signal using inverse multiscale local 1-D polynomial transform
`mlptdenoise`	Denoise signal using multiscale local 1-D polynomial transform

Topics

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).