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 (Since R2021b)
`liftingScheme`	Create lifting scheme for lifting wavelet transform (Since R2021a)
`liftingStep`	Create elementary lifting step (Since R2021a)
`lwt`	1-D lifting wavelet transform (Since R2021a)
`ilwt`	Inverse 1-D lifting wavelet transform (Since R2021a)
`laurentMatrix`	Create Laurent matrix (Since R2021b)
`laurentPolynomial`	Create Laurent polynomial (Since R2021b)
`liftfilt`	Apply elementary lifting steps on filters (Since R2021b)
`lwt2`	2-D Lifting wavelet transform (Since R2021b)
`ilwt2`	Inverse 2-D lifting wavelet transform (Since R2021b)
`lwtcoef`	Extract or reconstruct 1-D LWT wavelet coefficients and orthogonal projections (Since R2021a)
`lwtcoef2`	Extract 2-D LWT wavelet coefficients and orthogonal projections (Since R2021b)
`wave2lp`	Laurent polynomials associated with wavelet (Since R2021b)

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
Use lifting to design wavelet filters while performing the discrete wavelet 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).