US2014064615A1PendingUtilityA1
Method and Device for Denoising Videos Based on Non-Local Means
Est. expirySep 5, 2032(~6.1 yrs left)· nominal 20-yr term from priority
G06T 5/40G06T 2207/20182G06T 2207/20021G06T 2207/10016G06T 5/70
50
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Claims
Abstract
Disclosed is a method and a device for denoising a video based on non-local means, which is capable of making self-adaptive adjustment in response to illumination variance of the frame in the video.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A computer-implemented method for denoising a video based on non-local means, comprising:
constructing a local image block centered on a pixel i in a present frame of the video; delimiting a search area centered on the pixel as a search window; constructing respective search window for a pixel j in each of remaining frames of the video in same way as the present frame, wherein the constructed search windows constitute a three-dimensional search space, and carrying out, in reference to the search window of the present frame, a histogram normalization filtering process of the search windows of the remaining frames, so as to obtain a three-dimensional search space with illumination invariance.
2 . The method of claim 1 , wherein the step of delimiting further comprises:
constructing the search window centered on the pixel in a present frame, which is larger than a range of the local image block.
3 . The method of claim 2 , wherein the step of carrying further comprises:
setting νout j =G −1 (T(νin j )) where, νin j is the image value of the pixel j before the filtering, νout j is the image value of the pixel j after filtering, T(ν in )=∫ 0 ν in p in (w)dw, p in (w) is a probability density of a histogram of νin j distributed at a luminance level of w, G −1 is an inverse function of function G, wherein G(T(ν in ))=∫ 0 T(ν in )p out (t)dt, p out (t) is a probability density of a histogram of νout j distributed at a luminance level of t.
4 . The method of claim 3 , further comprising:
determining a similarity weight between the pixel i and the pixel j by calculating structural differences between the local image block of the present frame and all other local image blocks in the three-dimensional search space, and denoising the pixel i based on non-local means according to the determined similarity weight.
5 . The method of claim 4 , wherein the step of determining a similarity weight is carried out by rule of:
ω
(
,
j
)
=
1
Z
(
)
exp
(
-
v
(
B
i
)
-
v
(
B
j
)
2
,
a
2
h
2
)
where, ω(i,j) is the similarity weight between the pixels i and j,
B i and B j represent local image blocks centered on the pixels i and j, respectively,
ν(B i ) and ν(B j ) represent vectors constituted by values of pixels in local image blocks B i and B j corresponding to the pixels i and j, respectively,
∥•∥ 2,a 2 indicates the weighted Euclidean Distance between two vectors ν(B i ) and ν(B j ), in which symbol a means a spatial weight distribution which conforms to a Gaussian Distribution with its variance of a,
exp represents an exponential function,
Z
(
)
=
∑
j
∈
W
exp
(
-
v
(
B
i
)
-
v
(
B
j
)
2
,
a
2
h
2
)
,
h is a designated constant and is optimized according to different videos for controlling attributes of weight calculation.
6 . The method of claim 5 , wherein the step of denoising the pixel i based on non-local means according to the determined similarity weight is carried out by rule of:
NL
[
v
(
)
]
=
∑
j
∈
W
f
ω
(
,
j
)
v
(
j
)
,
where, NL[ν(i)] is a replaced image value of the pixel i, and
W f represents the three-dimensional search space.
7 . A device for denoising a video based on non-local means, comprising one or more processor configured to run a space module to
construct a local image block centered on a pixel i in a present frame of the video; delimit a search area centered on the pixel as a search window; construct respective search window for a pixel j in each of remaining frames of the video in same way as the present frame, wherein the constructed search windows constitute a three-dimensional search space, and the processor is further configured to run a histogram module to carry out, in reference to the search window of the present frame, a histogram normalization filtering process of the search windows of the remaining frames, so as to obtain a three-dimensional search space with illumination invariance.
8 . The device of claim 7 , wherein the space module is configured to construct the search window centered on the pixel in a present frame, which is larger than a range of the local image block.
9 . The device of claim 7 , wherein the histogram module is configured to carry out the histogram normalization filtering process by rule of νout j =G −1 (T(νin j )),
where, νin j is the image value of the pixel j before the filtering,
νout j is the image value of the pixel j after filtering,
T(ν in )=∫ 0 ν in p in (w)dw, p in (w) is a probability density of a histogram of νin j distributed at a luminance level of w,
G −1 is an inverse function of function G, wherein G(T(ν in ))=∫ 0 T(ν in )p out (t)dt, p out (t) is a probability density of a histogram of νout j distributed at a luminance level of t.
10 . The device of claim 9 , wherein the histogram module is configured to determine a similarity weight between the pixel i and the pixel j by calculating structural differences between the local image block of the present frame and all other local image blocks in the three-dimensional search space, and denoises the pixel i based on non-local means according to the determined similarity weight.
11 . The device of claim 10 , wherein the histogram module is configured to determine the similarity weight by rule of:
ω
(
,
j
)
=
1
Z
(
)
exp
(
-
v
(
B
i
)
-
v
(
B
j
)
2
,
a
2
h
2
)
where, ω(i,j) is the similarity weight between the pixels i and j,
B i and B j represent local image blocks centered on the pixels i and j, respectively,
ν(B i ) and ν(B j ) represent vectors constituted by values of pixels in local image blocks B i and B j corresponding to the pixels i and j, respectively,
∥•∥ 2,a 2 indicates the weighted Euclidean Distance between two vectors ν(B i ) and ν(B j ), in which symbol a means a spatial weight distribution which conforms to a Gaussian Distribution with its variance of a,
exp represents an exponential function,
Z
(
)
=
∑
j
∈
W
exp
(
-
v
(
B
i
)
-
v
(
B
j
)
2
,
a
2
h
2
)
,
h is a designated constant and is optimized according to different videos for controlling attributes of weight calculation.
12 . The device of claim 10 , wherein the histogram module is configured to denoise the pixel i based on non-local means by rule of:
NL
[
v
(
)
]
=
∑
j
∈
W
f
ω
(
,
j
)
v
(
j
)
where, NL[ν(i)] is a replaced image value of the pixel i, and
W f represents the three-dimensional search space.Join the waitlist — get patent alerts
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