Ref: J. C. Sprott, Comput. & Graphics
**28**,
113-117 (2004)

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Fig. 1. Hypothetical two-component landscape on a 208 x 208 grid
producted
by a stochastic cellular automaton after 1000 iterations with random
initial
conditions. When a 60 x 60 block of data is removed from the center,
the
plausibly realistic image on the right is generated after 5000
iterations
of a stochastic cellular automaton with replacements chosen randomly
from
an eight-cell neighborhood.

Fig. 2. Cluster probability produced by a two-component cellular
automaton
with replacements chosen randomly from different neighborhood sizes.

Fig. 3. The fraction of correctly identified cells in Fig. 1 is
about
70% at the boundary and smoothly degrades to about 50% as the boundary
recedes.

Fig. 4. The eight-level satellite data on a 548 x 548 grid of leaf
area
index over the Eastern United States on the left (courtesy of Steven
Running,
MODIS Land Group Member, University of Montana) is assumed to have a
160
x 160 block of data missing from the center and is reconstructed with
1000
iterations of a stochastic cellular automaton, producing the image on
the
right.

Fig. 5. The 256-color dithered image of a cat on the left is assumed
to have 400 random blocks of 10 x 10 pixels removed and then replaced
after
1000 iterations of a stochastic cellular automaton, producing the image
on the right.

Fig. 6. The 256-color dithered image of the Matterhorn on the left
is
assumed to have 25%, 50%, and 75% of the pixels removed in random
blocks
of 10 x 10 pixels and then replaced after 1000 iterations of a
stochastic
cellular automaton, producing the images on the right.