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We've learned how the discrete wavelet transform can be used
to analyze, or decompose, signals and images. This process is called *decomposition* or *analysis*.
The other half of the story is how those components can be assembled
back into the original signal without loss of information. This process
is called *reconstruction*, or *synthesis*. The mathematical manipulation that
effects synthesis is called the *inverse discrete
wavelet transform* (IDWT).

To synthesize a signal using Wavelet Toolbox™ software, we reconstruct it from the wavelet coefficients.

Where wavelet analysis involves filtering and downsampling, the wavelet reconstruction process consists of upsampling and filtering. Upsampling is the process of lengthening a signal component by inserting zeros between samples.

The toolbox includes commands, like `idwt`

and `waverec`

, that perform single-level or
multilevel reconstruction, respectively, on the components of 1-D
signals. These commands have their 2-D and 3-D analogs, `idwt2`

, `waverec2`

, `idwt3`

, and `waverec3`

.

The filtering part of the reconstruction process also bears some discussion, because it is the choice of filters that is crucial in achieving perfect reconstruction of the original signal.

The downsampling of the signal components performed during the decomposition phase introduces a distortion called aliasing. It turns out that by carefully choosing filters for the decomposition and reconstruction phases that are closely related (but not identical), we can “cancel out” the effects of aliasing.

A technical discussion of how to design these filters is available
on page 347 of the book *Wavelets and Filter Banks*,
by Strang and Nguyen. The low- and high-pass decomposition filters
(`L`

and `H`

), together with their
associated reconstruction filters (`L'`

and `H'`

),
form a system of what is called *quadrature mirror filters**:*

We have seen that it is possible to reconstruct our original signal from the coefficients of the approximations and details.

It is also possible to reconstruct the approximations and details
themselves from their coefficient vectors. As an example, let's consider
how we would reconstruct the first-level approximation `A1`

from
the coefficient vector `cA1`

.

We pass the coefficient vector `cA1`

through
the same process we used to reconstruct the original signal. However,
instead of combining it with the level-one detail `cD1`

,
we feed in a vector of zeros in place of the detail coefficients vector:

The process yields a reconstructed *approximation* `A1`

,
which has the same length as the original signal `S`

and
which is a real approximation of it.

Similarly, we can reconstruct the first-level detail `D1`

,
using the analogous process:

The reconstructed details and approximations are true constituents of the original signal. In fact, we find when we combine them that

*A*_{1} + *D*_{1} = *S*.

Note that the coefficient vectors `cA1`

and `cD1`

—
because they were produced by downsampling and are only half the length
of the original signal — cannot directly be combined to reproduce
the signal. *It is necessary to reconstruct the approximations
and details before combining them.*

Extending this technique to the components of a multilevel analysis, we find that similar relationships hold for all the reconstructed signal constituents. That is, there are several ways to reassemble the original signal:

In the section Reconstruction Filters, we spoke of the importance of choosing the right filters. In fact, the choice of filters not only determines whether perfect reconstruction is possible, it also determines the shape of the wavelet we use to perform the analysis.

To construct a wavelet of some practical utility, you seldom start by drawing a waveform. Instead, it usually makes more sense to design the appropriate quadrature mirror filters, and then use them to create the waveform. Let's see how this is done by focusing on an example.

Consider the low-pass reconstruction filter (`L'`

)
for the `db2`

wavelet.

The filter coefficients can be obtained from the `dbaux`

function. By reversing the order
of the scaling filter vector and multiplying every even element (indexing
from 1) by (-1), you obtain the high-pass filter.

Repeatedly upsampling by two and convolving the output with the scaling filter produces the Daubechies' extremal phase wavelet.

L = dbaux(2); H = wrev(L).*[1 -1 1 -1]; HU = dyadup(H,0); HU = conv(HU,L); plot(HU); title('1st Iteration'); H1 = conv(dyadup(HU,0),L); H2 = conv(dyadup(H1,0),L); H3 = conv(dyadup(H2,0),L); H4 = conv(dyadup(H3,0),L); figure; for k =1:4 subplot(2,2,k); eval(['plot(H' num2str(k) ')']); axis tight; end

The curve begins to look progressively more like the `db2`

wavelet.
This means that the wavelet's shape is determined entirely by the
coefficients of the reconstruction filters.

This relationship has profound implications. It means that you cannot choose just any shape, call it a wavelet, and perform an analysis. At least, you can't choose an arbitrary wavelet waveform if you want to be able to reconstruct the original signal accurately. You are compelled to choose a shape determined by quadrature mirror decomposition filters.

We've seen the interrelation of wavelets and quadrature mirror filters. The wavelet function ψ is determined by the high-pass filter, which also produces the details of the wavelet decomposition.

There is an additional function associated with some, but not
all, wavelets. This is the so-called *scaling function*,
ϕ. The scaling function is very similar to the wavelet function.
It is determined by the low-pass quadrature mirror filters, and thus is associated with
the approximations of the wavelet decomposition.

In the same way that iteratively upsampling and convolving the high-pass filter produces a shape approximating the wavelet function, iteratively upsampling and convolving the low-pass filter produces a shape approximating the scaling function.

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