.Wavelet.Types .Wavelet.TypesEnumeration variables are defined here.
| waveletID | Wavelet name |
| 1 | Haar |
| 2 | Daubechies |
| 3 | Symlets |
| 4 | Coiflets |
| 5 | Biorthogonal spline |
| 6 | Reverse biorthogonal spline |
| 7 | Meyer |
| 8 | Discrete Meyer |
| 9 | Gaussian |
| 10 | Mexican hat |
| 11 | Morlet |
| 12 | Complex Gaussian |
| 13 | Complex Morlet |
| 14 | Complex Shannon |
| 15 | Complex frequency B-Spline |
| Value | Name | Description |
| 1 | coefficients | Wavelet coefficients |
| 2 | signal | Reconstructed signal |
| Value | Name | Description |
| 1 | fixedForm | Fixed form threshold. Threshold value = sqrt(2*ln(N)), with N being the length of the data to be denoised. |
| 2 | SURE | The threshold is calculated using the principle of Stein's Unbiased Risk Estimate(SURE). |
| 3 | heurSure | Heuristic SURE method. It is a combination of fixedForm and SURE methods. The noise level is firstly tested. For a high signal-to-noise ratio, SURE method is used, otherwise fixedForm method is used. |
| 4 | miniMax | The threshold is calculated according to the miniMax principle, which realizes the minimum of the maximum mean square error. |
| Name | Description |
|---|---|
 mraDisplay | Selection of display data for MRA: 1 - wavelet coefficients; 2 - reconstructed signal |
 threshMethod | Methods for calculating threshold for denoising: 1 - fixed form; 2 - SURE method; 3 - heuristic SURE; 4 - minimal maximum method |
 wavletID | Definition of wavelet identifiers: 1 - Haar; 2 - Daubechies; 3 - Symlets; 4 - Coiflets; 5 - Biorthogonal spline; 6 - Reverse biorthogonal spline; 7 - Meyer; 8 - Discrete Meyer; 9 - Gaussian; 10 - Mexican hat; 11 - Morlet; 12 - Complex Gaussian; 13 - Complex Morlet; 14 - Complex Shannon; 15 - Complex frequency B-Spline |