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.
|Filters to Laurent polynomials (Since R2021b)
|Create lifting scheme for lifting wavelet transform (Since R2021a)
|Create elementary lifting step (Since R2021a)
|1-D lifting wavelet transform (Since R2021a)
|Inverse 1-D lifting wavelet transform (Since R2021a)
|Create Laurent matrix (Since R2021b)
|Create Laurent polynomial (Since R2021b)
|Apply elementary lifting steps on filters (Since R2021b)
|2-D Lifting wavelet transform (Since R2021b)
|Inverse 2-D lifting wavelet transform (Since R2021b)
|Extract or reconstruct 1-D LWT wavelet coefficients and orthogonal projections (Since R2021a)
|Extract 2-D LWT wavelet coefficients and orthogonal projections (Since R2021b)
|Laurent polynomials associated with wavelet (Since R2021b)
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).