## Documentation Center |

This section takes you through the features of local thresholding of wavelet coefficients for one-dimensional signals or data. This capability is available through graphical interface tools throughout the Wavelet Toolbox™ software:

Wavelet De-noising 1-D

Wavelet Compression 1-D

SWT De-noising 1-D

Regression Estimation 1-D

Density Estimation 1-D

This tool allows you to define, level by level, time-dependent
(x-axis-dependent) thresholds, and then increase the capability of
the de-noising strategies handling nonstationary variance noise. More
precisely, the model assumes that the observation is equal to the
interesting signal superimposed on noise. The noise variance can vary
with time. There are several different variance values on several
time intervals. The values as well as the intervals are unknown. This
section will use one of the graphical interface tool (**SWT De-noising 1-D**) to illustrate this capability.
The behavior of all the above-mentioned tools is similar.

From the MATLAB

^{®}prompt, typewavemenu

The

**Wavelet Toolbox****Main Menu**appears.Click the

**SWT De-noising 1-D**menu item.The discrete stationary wavelet transform de-noising tool for one-dimensional signals appears.

From the

**File**menu, choose the**Load Signal**option.When the

**Load Signal**dialog box appears, select the MAT-file`nblocr1.mat`, which should reside in the MATLAB folder`toolbox/wavelet/wavedemo`. Click the**OK**button. The noisy blocks signal with two change points in the noise variance located at positions 200 and 600, is loaded into the**SWT De-noising 1-D**tool.Select the

`db1`wavelet from the**Wavelet**menu and select**5**from the**Level**menu, and then click the**Decompose Signal**button. After a pause for computation, the tool displays the stationary wavelet approximation and detail coefficients of the decomposition.Accept the defaults of

**Fixed form soft**thresholding and**Unscaled white noise**. Click the**De-noise**button.The result is quite satisfactory, but seems to be oversmoothed when the signal is irregular.

Select

**hard**for the thresholding mode instead of**soft**, and then click the**De-noise**button.The result is not satisfactory. The denoised signal remains noisy before position 200 and after position 700. This illustrates the limits of the classical de-noising strategies. In addition, the residuals obtained during the last trials clearly suggest to try a local thresholding strategy.

Generate interval-dependent thresholds.

Click the

**Int. dependent threshold Settings**button located at the bottom of the thresholding method frame. A new window titled**Int. Dependent Threshold Settings for figure ...**appears.Click the

**Generate**button. After a pause for computation, the tool displays the default intervals associated with adapted thresholds.Three intervals are proposed. Since the variances for the three intervals are very different, the optimization program easily detects the correct structure. Nevertheless, you can visualize the intervals proposed for a number of intervals from 1 to 6 using the

**Select Number of Intervals**menu (which replaces the**Generate**button). Using the default intervals automatically propagates the interval delimiters and associated thresholds to all levels.

**Denoise with Interval-Dependent Thresholds. **Click the **Close** button in the **Int. Dependent Threshold Settings for ...** window.
When the **Update thresholds** dialog
box appears, click **Yes**. The **SWT De-noising 1-D** main window is updated.
The sliders located to the right of the window control the level and
interval dependent thresholds. For a given interval, the threshold
is indicated by yellow dotted lines running horizontally through the
graphs on the left of the window. The red dotted lines running vertically
through the graphs indicate the interval delimiters. Next click the **De-noise** button.

**Modifying Interval Dependent Thresholds. **The thresholds can be increased to keep only the highest values
of the wavelet coefficients at each level. Do this by dragging the
yellow lines directly on the graphs on the left of the window, or
using the **View Axes** button (located
at the bottom of the screen near the **Close** button),
which allows you to see each axis in full size. Another way is to
edit the thresholds by selecting the interval number located near
the sliders and typing the desired value.

Note that you can also change the interval limits by holding down the left mouse button over the vertical dotted red lines, and dragging them.

You can also define your own interval
dependent strategy. Click the **Int. dependent
threshold settings** button. The **Int.
Dependent Threshold Settings for ...** window appears again.
We shall explore this window for a little while. Click the **Delete** button, so that the interval delimiters
disappear. Double click the left mouse button to define new interval
delimiters; for example at positions 300 and 500 and adjust the thresholds
manually. Each level must be considered separately using the **Level** menu for adjusting the thresholds. The
current interval delimiters can be propagated to all levels by clicking
the **Propagate** button. So click the **Propagate** button. Adjust the thresholds for
each level, one by one. At the end, click the **Close** button
of the **Int. Dependent Threshold settings for
...** window. When the **Update thresholds** dialog
box appears, click **Yes**. Then click
the **denoise** button.

Note that

By double-clicking again on an interval delimiter with the left mouse button, you delete it.

You can move the interval delimiters (vertical red dotted lines) and the threshold levels (horizontal yellow dotted lines) by holding down the left mouse button over these lines and dragging them.

The maximum number of interval delimiters at each level is 10.

From the **File** menu, choose
the **Example Analysis > Noisy Signals - Interval
Dependent Noise Variance > ** option. From the drop down
men, choose `with haar at level 4 ---> Elec. consumption
— 3 intervals`. The proposed items contain, in
addition to the usual information, the "true" number
of intervals. You can then experiment with various signals for which
local thresholding is needed.

The tool lets you save the denoised signal to disk. The toolbox creates a MAT-file in the current folder with a name you choose.

To save the denoised signal from the present de-noising process,
use the menu option **File > Save denoised
Signal**. A dialog box appears that lets you specify a folder
and filename for storing the signal. Type the name `dnelec`.
After saving the signal data to the file `dnelec.mat`,
load the variables into your workspace:

load dnelec whos

Name | Size | Bytes | Class |
---|---|---|---|

dnelec | 1x2000 | 16000 | double array |

thrParams | 1x4 | 656 | cell array |

wname | 1x4 | 8 | char array |

The denoised signal is given by `dnelec`. In
addition, the parameters of the de-noising process are given by the
wavelet name contained in wname:

wname wname = haar

and the level dependent thresholds contained in `thrParams`,
which is a cell array of length 4 (the level of the decomposition).
For `i` from 1 to 4, `thrParams{i}` is
an array `nbintx3` (where `nbint` is
the number of intervals, here 3), and each row contains the lower
and upper bounds of the interval of thresholding and the threshold
value. For example, for level 1,

thrParams{1} ans = 1.0e+03 * 0.0010 0.0980 0.0060 0.0980 1.1240 0.0204 1.1240 2.0000 0.0049

Was this topic helpful?