Symmlets
Symmlets, also known as Symlet wavelets, are a family of wavelet functions developed by Ingrid Daubechies in the late 20th century. They are a variation of Daubechies wavelets designed to improve symmetry properties, which are advantageous in signal processing applications such as data compression, noise reduction, and feature extraction.
Symmlets are characterized by their near-symmetry and minimal phase distortion, making them more suitable than Daubechies
The construction of Symmlet wavelets involves polynomial filters with specific symmetry constraints, resulting in wavelets that
Symmlets are popular in various applications, including image processing, where their symmetry reduces artifacts, and in
Overall, Symmlets offer an effective alternative to other wavelet bases, especially when symmetry and phase considerations