Learning Iterative Image Reconstruction - Hierarchical Neural Networks for Image Interpretation - page 188

Fig. 9.18. Noise removal and contrast enhancement. The activities of all feature arrays are
shown over time. A hierarchical distributed representation of the digit stabilizes. It is used to
amplify the lines and to remove background clutter.
A distributed representation of the digit stabilizes in the hierarchical network.
Most interesting is the representation of the background level. Three features in
Layer 1, one feature in Layer 2, and one feature in Layer 3 seem to estimate the
background intensity. The remaining feature arrays form a sparse representation of
other digit features.
The progress of the reconstruction process is shown in Figure 9.19 for the first
ten test examples. One can observe that the network is able to detect dark lines,
to complete them, to remove background clutter, and to enhance the contrast. The
interpretation at most locations is decided quickly by the network. Ambiguous loca-
tions are kept for some iterations at intermediate values, such that the decision can
be influenced by neighboring locations. The reconstructed digits are very similar to
the originals.
To illustrate the network's solution to the reconstruction problem, Figure 9.20
shows the activities of the single hidden feature in the networks's bottom layer
and the output feature array together with its contributions for the same digits after
twelve iterations.
One can observe that the hidden units are less confident than the output units.
The hidden feature array seems to represent potential lines. Some background struc-
tures are still visible there since adjacent dark pixels could be caused by the presence
of a line, rather than by noise.