US 6,999,634 B2Granted

Spatio-temporal joint filter for noise reduction

AssigneeLG Electronics Inc.

InventorSung Hoon Hong

Life of this patent

Estimating the term…

Abstract

A spatio-temporal joint filter and a spatial joint filter for noise reduction are disclosed. The spatio-temporal joint filter includes a spatial joint filter including the first and second sub filters having different characteristics and includes a temporal joint filter. When the present invention is adequately used, an edge/detail region of an image is well preserved, an aggressive noise reduction is performed on a flat region, and the temporal flicker problems are eliminated. Additionally, it has an intrinsic motion compensation effect by using the spatio-temporal correlation between the adjacent frames.

Claims11

Dependent claims

A spatio-temporal joint filter for noise reduction comprising:

a spatial joint filter including
a first filter connected to an input determining a first weighted average of current support region signals of said input using first weighting factors, said current support region signals being included in a current frame, and
a second filter connected to said input and an output of said first filter determining a second weighted sum of said input signal and said first weighted average using a second weighting factor that depends on an original signal variance and a noise variance;
a temporal joint filter including
a third filter connected to an output of said second filter determining a third weighted average of said second weighted sum and previous support region signals of said input signal using third weighting factors, said previous support region signals being included in a previous frame, and
a fourth filter connected to an output of said second filter determining a fourth weighted sum of said second weighted sum and said third weighted average using a fourth weighting factor that depends on a spatio-temporal signal variance and said noise variance wherein said fourth weighted sum fo (i,j,t) is determined using the following equation:
α
⁡
(
i
,
j
,
t
)
=
σ
f
2
⁡
(
i
,
j
,
t
)
σ
f
2
⁡
(
i
,
j
,
t
)
+
σ
n
2
where
fs(i,j,t) is said second weighted sum,
μT(i,j,t) is said third weighted average,
σ2ST (i,j,t) is said spatio-temporal signal variance, and
σ2n is a noise variance;
σ
ST
2
⁡
(
i
,
j
,
t
)
=
max
⁡
[
σ
f
2
⁡
(
i
,
j
,
t
)
,
σ
d
2
⁡
(
i
,
j
,
t
)
-
σ
n
2
2
]
σ
d
2
⁡
(
i
,
j
,
t
)
=
1
S
⁢
{
∑
(
i
,
j
)
∈
S
⁢
(
g
⁡
(
i
,
j
,
t
)
-
f
o
⁡
(
i
,
j
,
t
-
1
)
)
2
}
_
where
σ2f (i,j,t) is a original signal variance, and
fo(l,m,t−1) is a previous fourth weighted sum included in said support region of said previous frame;
and
a frame memory storing said fourth weighted sum and feedbacking said fourth weighted sum to said third filter for a next frame.

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Dependencies are inferred from the claim text.

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Abstract

A spatio-temporal joint filter for noise reduction comprising:

a spatial joint filter including
a first filter connected to an input determining a first weighted average of current support region signals of said input using first weighting factors, said current support region signals being included in a current frame, and
a second filter connected to said input and an output of said first filter determining a second weighted sum of said input signal and said first weighted average using a second weighting factor that depends on an original signal variance and a noise variance;
a temporal joint filter including
a third filter connected to an output of said second filter determining a third weighted average of said second weighted sum and previous support region signals of said input signal using third weighting factors, said previous support region signals being included in a previous frame, and
a fourth filter connected to an output of said second filter determining a fourth weighted sum of said second weighted sum and said third weighted average using a fourth weighting factor that depends on a spatio-temporal signal variance and said noise variance wherein said fourth weighted sum fo (i,j,t) is determined using the following equation:
α
⁡
(
i
,
j
,
t
)
=
σ
f
2
⁡
(
i
,
j
,
t
)
σ
f
2
⁡
(
i
,
j
,
t
)
+
σ
n
2
where
fs(i,j,t) is said second weighted sum,
μT(i,j,t) is said third weighted average,
σ2ST (i,j,t) is said spatio-temporal signal variance, and
σ2n is a noise variance;
σ
ST
2
⁡
(
i
,
j
,
t
)
=
max
⁡
[
σ
f
2
⁡
(
i
,
j
,
t
)
,
σ
d
2
⁡
(
i
,
j
,
t
)
-
σ
n
2
2
]
σ
d
2
⁡
(
i
,
j
,
t
)
=
1
S
⁢
{
∑
(
i
,
j
)
∈
S
⁢
(
g
⁡
(
i
,
j
,
t
)
-
f
o
⁡
(
i
,
j
,
t
-
1
)
)
2
}
_
where
σ2f (i,j,t) is a original signal variance, and
fo(l,m,t−1) is a previous fourth weighted sum included in said support region of said previous frame;
and
a frame memory storing said fourth weighted sum and feedbacking said fourth weighted sum to said third filter for a next frame.

145 more elements