Digital Signal Processing Reference
In-Depth Information
a
Measurement matrix
10
20
30
40
50
20
40
60
80
100
120
140
160
180
200
c
b
Original sparse signal
Compressive measurements
80
100
60
90
80
40
70
20
60
50
0
40
−20
30
−40
20
10
−60
0
−80
0
20
40
60
80
100
120
140
160
180
200
0
5
10
15
20
25
30
35
40
45
50
d
e
l
1
recovery
l
2
recovery
150
100
90
100
80
70
50
60
50
0
40
−50
30
20
−100
10
0
−150
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
f
g
l
1
reconstruction error
l
2
reconstruction error
2.5
x 10
−4
150
2
100
1.5
50
1
0
0.5
−50
0
−100
−0.5
−1
−150
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
Fig. 2.3
1D sparse signal recovery example from random Gaussian measurements.
(a)
Com-
pressive measurement matrix.
(b)
Original sparse signal.
(c)
Compressive measurements.
(d)
1
recovery.
(e)
2
recovery.
(f)
1
reconstruction error.
(g)
2
reconstruction error
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