Television – Image signal processing circuitry specific to television – Noise or undesired signal reduction
Reexamination Certificate
1999-10-08
2002-08-13
Hsia, Sherrie (Department: 2614)
Television
Image signal processing circuitry specific to television
Noise or undesired signal reduction
C348S619000
Reexamination Certificate
active
06433834
ABSTRACT:
BACKGROUND OF THE INVENTION
Field of the Invention
The invention lies in the electronics field. More specifically, the invention pertains to a method of suppressing signal noise.
In many cases, undesirable noise is superimposed on a signal, either during the recording (acquisition) of the signal or in the course of transmission. The signals may be one-dimensional, for example voice signals; or two-dimensional, for example stationary images; or three-dimensional, for example picture sequences. In general, the problem of noise suppression may be described as follows:
A noise signal is additively superimposed on a useful signal (i.e., the wanted signal) S
0
(x,y,t):
S
(
x,y,t
)=
S
0
(
x,y,t
)+
R
(
x,y,t
)
The question is: how to obtain a good estimate for S
0
(x,y,t) when S(x,y,t) is measured (that is known) and, possibly, when the statistical characteristics of R(x,y,t) are known.
Many noise suppression algorithms presuppose a constant signal and, as a rule, simply average the observed signal. In that case, it is generally assumed that the useful signal has a narrower bandwidth than the noise. The signal-to-noise ratio can be improved by a low-pass filter, such as an averaging filter. However, the assumption of a narrower bandwidth actually results in a problem with such a procedure since the details in a picture or the high frequencies in music are likewise located in the high frequency area. As a rule, they suffer from simple averaging. The requirement is thus for filters with the characteristic of retaining detail.
The most common methods in the prior art are so-called signal-adaptive methods. There, the aim is to detect the signal dynamics in the observed signal in order to average more strongly when major signal changes are present than for minor changes. As a rule, the weightings are in this case applied directly to the measured signal. For example, the normal practice in image processing is to look for specific values which have little signal dynamics. An averaging filter is then applied to these signal dynamics.
By way of example: Averaging of three pixels, which may be described as a homogeneous field.
The disadvantage in that case is that the other points are ignored, even though they likewise contain more or less information which could contribute to improving the estimated values.
SUMMARY OF THE INVENTION
It is accordingly an object of the invention to provide a method of suppressing signal noise, which overcomes the above-mentioned disadvantages of the heretofore-known methods of this general type and which suppressing noise in one-dimensional or multidimensional signals, in which the high frequency areas are not so heavily attenuated, so that corresponding details are retained.
With the foregoing and other objects in view there is provided, in accordance with the invention, a method of suppressing noise in a signal, which comprises: measuring a noisy signal S(x,y,t) and carrying out a plurality of estimates for a corresponding noise-free useful signal S
0
(x,y,t) on a basis of the measurement of the noisy signal S(x,y,t);
individually assigning each estimated value S
0
(1)
, S
0
(1)
, S
0
(2)
, . . . , S
0
(k)
obtained for each point (x,y,t) in carrying out the plurality of estimates a preference &agr;
j
(x,y,t), where j=1,2, . . . , k; and k is an integer; and forming a new estimated value {overscore (S)}
0
(x,y,t) for the useful signal from the estimated values S
0
(1)
, S
0
(2)
, . . . , S
0
(k)
by arithmetic averaging weighted by the preference &agr;
j
(x,y,t).
In other words, the object of the invention is satisfied in that a plurality of estimates for the noise-free useful signal are carried out on the basis of a measurement of the noisy signal, and each of the estimated values obtained for each point is individually assigned a preference, and a new estimated value for the useful signal is then formed from the estimated values by arithmetic averaging weighted by the preference.
In accordance with an added feature of the invention, the estimated value {overscore (S)}
0
(x,y,t) is calculated with the following formula:
S
o
_
(
x
,
y
,
t
)
=
∑
j
=
1
k
α
j
(
x
,
y
,
t
)
A
S
o
(
j
)
(
x
,
y
,
t
)
,
where
A
=
∑
j
=
1
k
α
j
(
x
,
y
,
t
)
.
In accordance with an additional feature of the invention, the preference &agr;
j
(x,y,t) is determined by considering statistical characteristics of the noise.
In accordance with a further feature of the invention, the signal is a one-dimensional signal S(t) and the method comprises determining one of the estimated values with an earlier-measured value, determining a further estimated value with a later-measured value, and determining the weighting factors or preference values &agr;
j
associated with the estimated values as a function of the signal.
In accordance with a preferred embodiment, the following estimated values and preferences are defined for the one-dimension signal:
S
(
1
)
(
t
0
)
=
1
2
[
S
(
t
0
)
+
S
(
t
0
+
1
)
]
,
α
1
(
t
0
)
=
1
[
S
(
t
0
)
-
S
(
1
)
(
t
0
)
]
2
;
S
(
2
)
t
0
=
1
2
[
S
(
t
0
)
+
S
(
t
0
-
1
)
]
;
α
2
(
t
0
)
=
1
[
S
(
t
0
)
-
S
(
2
)
(
t
0
)
]
2
;
S
(
3
)
t
0
=
S
(
t
0
)
;
α
3
(
t
0
)
=
1
σ
n
2
and
S
_
(
t
0
)
=
∑
i
=
1
3
α
i
·
S
(
i
)
(
t
0
)
∑
i
=
1
3
α
j
where &sgr;
n
2
is selected from the group of consisting of a statistical variance of the noise and a value to be set by the user.
In accordance with another feature of the invention, the signal is a two-dimensional signal S(x,y) and the method comprises determining further estimated values using adjacent measured signal values and are weighted by signal-dependent weighting factors or preference values &agr;
j
.
In accordance with a preferred embodiment of the invention, mean values S
(1)
(x
0
,y
0
); . . . ; S
(8)
(x
0
,y
0
) are formed from the measured signal value S(x
0
,y
0
) of the two-dimensional signal, and respective adjacent measured signal values S(x
0
−1,y
0
); S(x
0
+1,y
0
); S(x
0
−1,y
0
+1); S(x
0
+1,y
0
+1); S(x
0
+1,y
0
−1); S(x
0
+1,y
0
−1); S(x
0
,y
0
+1); S(x
0
,y
0
−1) are weighted by a factor &agr;
j
, where
α
j
=
1
[
S
(
x
0
,
y
0
)
-
S
(
j
)
(
x
0
,
y
0
)
]
2
;
and the estimated value is determined from the mean value of the estimated values S
(1)
(x
0
,y
0
); . . . ; S
(8)
(x
0
,y
0
) weighted in such a way and the measured value S(x
0
,y
0
) weighted by a factor &agr;
9
which is predetermined by the user or is calculated using the formula
α
9
=
1
σ
n
2
,
where &sgr;
n
2
represents a statistical variance of the noise.
In other words, for two-dimensional signals it is particularly preferable for the mean values to be formed from the measured signal value and the eight respective adjacent measured signal values, and to be weighted in each case by a factor which is defined as the reciprocal of the square of the difference between the measured signal value and the respective mean value and for the new estimated value for the noise-free signal then to be determined from the mean value of the estimated values weighted by this factor and the measured value weighted by a factor which is predetermined by the user or is calculated using the statistical variance of the noise.
In accordance with again an added feature of the invention, the signal is a three-dimensional signal S(x
0
,y
0
,t
0
), such as a television picture signal, and the method comprises using an estimated result of a previous sampling time {overscore (S)}(x
0
,y
0
,t
0
−1) as the first estimated value S
(1)
(x
0
,y
0
,t
0
), using the mean value of a present pixel and of the points located in front of and behind the present pixel on the same line (median) {S(x
0
−1,y
Hsia Sherrie
Siemens Aktiengesellschaft
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