Discrete wavelet transform

### 21.4.10 Discrete wavelet transform

The dwt and idwt commands are used for computing the discrete wavelet transform (DWT) of a signal and for computing the inverse transformation, i.e. reconstructing the original signal from the transform. These commands require the GSL library.

- dwt and idwt both take one mandatory argument and up to four optional arguments (the order of optional arguments is irrelevant):
  - data, a numeric vector of length n or matrix with dimensions m× n.
  - Optionally, "wtype", a string which defines the wavelet family. Supported values of wtype are: daubechies (the default), haar and bspline.
  - Optionally, k, a positive integer which selects the specified member of the wavelet family. For the Daubechies wavelet family, k∈{4,8,…,20} (even numbers). For the Haar wavelet, only k=2 is supported. For the biorthogonal B-spline wavelet family, the implemented values of k are 103, 105, 202, 204, 206, 208, 301, 303, 305 307 and 309. By default, k=4 is selected for the Daubechies wavelet, k=2 for the Haar wavelet and k=103 for the B-spline wavelet.
  - Optionally, center, the symbol which makes use of the centered forms of the wavelets which align the coefficients of the various sub-bands on edges, making the resulting visualization of the coefficients of the wavelet transform in the phase plane easier to understand.
  - Optionally, image, the symbol which specifies the “non-standard” form of the 2D DWT.
  Note that the input array data will be enlarged, if necessary, such that its size is a power of two. If data is a matrix, it is augmented to a square matrix of size equal to the first power of 2 not smaller than max{m,n}. Input data is extended by zero-padding.
- If data is a vector, then dwt(data ⟨,options ⟩) returns the list of length n which contains the DWT of the signal data, packed in a triangular storage layout:

  ⎛ ⎝ | s−1,0,d0,0,d1,0,d1,1,d2,0,d2,1,d2,2,…,di,j,…,dN−1,2N−1−1 | ⎞ ⎠ | ,

  where i=0,…,N−1 is the level index, j=0,…,2i−1 is the index of the coefficient within each level, and N=log2(n). The first element s−1,0 is the smoothing coefficient, which is followed by detail coefficients di,j for each level i.
- If data is a matrix, then the “non-standard” 2D DWT is performed in interleaved passes on the rows and columns for each level of the transform. The non-standard form of DWT is typically used in image analysis. By default, the “standard” form is used, which performs a complete DWT on the rows of the matrix, followed by a separate complete DWT on the columns of the resulting row-transformed matrix.
- idwt takes the DWT and returns the original data. The data parameter may be modified prior to calling idwt for the sake of e.g. signal smoothing or data compression.

#### Examples

##### Signal compression.

In the first example (adapted from GSL documentation), we load a sample of size 256 from the MIT-BIH Arrhythmia Database (see here) and attempt to remove the noisy information.

data:=mid(col(csv2gen("/home/luka/Documents/MIT-BIH.csv","\t"),2),300,256):; listplot(data)

Now we transform the signal by using dwt and select the 20 largest components. We set other elements to zero and transform the result back using idwt.

tdata:=dwt(data):; p:=reverse(sortperm(abs(tdata))):; for k from 20 to 255 do tdata[p[k]]:=0; od:; res:=idwt(tdata):; listplot(res)

As you can see, the fast, low-amplitude oscillations are gone, but important features such as the spike are left practically intact. As a beneficial side-effect of denoising, you get the signal compressed by, in this case, about 92% in terms of memory usage.

##### Noise reduction in images.

Assuming that an image noisy.jpg is stored in Pictures folder, enter:

img:=image("/home/luka/Pictures/noisy.jpg")(grey)

an image of size 300×300 (grayscale) |  |  |  |  |  |  |  |  |  |

Use DWT to transform the image matrix and subsequently replace each component smaller than 55 (by absolute value) in the resulting matrix to zero, before transforming it back to obtain a noise-reduced image. (The threshold value was obtained by trial and error.) Note that dwt enlarges the input matrix to the size 512× 512, so be sure to crop the output of idwt back to 300× 300. To make sure that no component gets below zero or above 255, use the threshold command (see Section 21.3.4).

tdata:=dwt(img[0],image):; sdata:=threshold(tdata,55.0=0,'<=',abs=true):; nr:=image(1,threshold(round(subMat(idwt(sdata,image),0,0,299,299)),[0,255]))

an image of size 300×300 (grayscale) |  |  |  |  |  |  |  |  |  |

To display the original image and the processed image next to each other for comparison, enter:

display(img,0); display(nr,320); legend(-20i,"original"); legend(320-20i,"denoised")