Image Processing Reference
In-Depth Information
Re (
x
1
)
(a)
(b)
1
(c)
0.9
0.1
0.1
0
0.9
0.8
2
4
6
8
10
12
14
16
0.8
0.7
0.05
0.05
0.7
0.6
0.6
0.5
0
0.5
0
0.4
0.4
0.3
0.3
-0.05
-0.05
0.2
0.2
0.1
0.1
-0.1
-0.1
0
-0.1
-0.1
0
-0.05
0
0.05
0.1
0
5
10
15
-0.05
0
0.05
0.1
x
(m)
x
(m)
(d)
(e)
(f )
3
100
3
4
0.06
0.06
2.5
0.04
0.02
0.04
0.02
0
-0.02
-0.04
-0.06
50
2.5
PM
3
0
2
0
-0.02
-0.04
-0.06
2
2
-50
1.5
1.5
1
-100
-100
1
-50
0
50
100
-0.05
0
0.05
-0.05
0
0.05
x
(mm)
(h)
(g)
(i)
y
100
3
50
2.5
0
2
x
-50
1.5
-100
-100
1
0
1
2
0
4
-50
0
50
100
0.08
2.6
0.0
Re{τ(
x
,
y
)}
x
(mm)
y
(j)
(k)
(l)
250
0.2
0.1
0
0.2
0.4
0.6
0.8
1.0
2.0
200
0.05
150
100
1.0
x
0
-0.05
50
0.0
-0.
-0.1
-0.05
50
100
150
200
250
0
0.05
2.6
Re{τ(
x
,
y
)}
0.0
x
(m)
Figure 9.24
Comparison of reconstruction algorithms on FoamTwinDiel: (a) two-step inexact Newton method
(2 GHz), (b) two-step inexact Newton method (5 GHz), (c) CSI method, (d) MGM Method, (e) MGM method with adap-
tive multiscale, (f) DTA/CSI methods, (g) DTA/CSI methods, (h) Bayesian inversion method, (i) IMSA (plane wave)
method, (j) IMSA (line source) method, (k) General Iterative method, and (l) cepstral method.
that more data is needed. Even with the limited amount of data, the cepstral
method still seems to be able to isolate contrast difference between all three
cylinders.
Figure 9.25 shows the comparison of different reconstruction methods for
FoamMetExt data. The two-step Newton method (Estatico et al., 2005), which
attempted to reconstruct the first three objects, was not able to show any
reconstruction for the metal object. Also the iterative method proposed in
Litman (2005) did not present any reconstruction for the metal object. Figure
9.25a based on the CSI Method (van den Berg et al., 1999) seems to show good
reconstruction for the shape, but does not seem to provide any quantitative
description. Figure 9.25d seems to show decent performance for both shape
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