US2014363090A1PendingUtilityA1
Methods for Performing Fast Detail-Preserving Image Filtering
Est. expiryFeb 24, 2031(~4.5 yrs left)· nominal 20-yr term from priority
Inventors:Rastislav Lukac
G06T 2207/20192G06T 5/20G06T 5/002G06T 2207/20012G06T 5/70
54
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Claims
Abstract
A method for performing fast detail-preserving filtering of an input digital image includes, for each pixel in the image, calculating the difference between the selected pixel and each of its four neighboring pixels located above, left, right, and below the selected pixel, calculating a scaled weighted sum of differences between the actual pixel and its four neighbors, where for each neighboring pixel the weight is an edge-sensing function having a data-adaptive scaling parameter function, and adding the weighted sum of differences to the value of the selected pixel.
Claims
exact text as granted — not AI-modified1 - 18 . (canceled)
19 . A method for creating an output digital image by performing fast detail-preserving filtering of an input digital image comprising:
a) selecting a pixel a from the input digital image, pixel a not being in a row or column at the boundary of the image; b) calculating the difference between the selected pixel and each of its four neighboring pixels located above, left, right, and below the selected pixel; c) calculating a scaled weighted sum of differences between the selected pixel and its four neighbors, where for each neighboring pixel b the weight is an edge-sensing function having a data-adaptive scaling parameter function; d) adding the weighted sum of differences to the value of the selected pixel; and e) performing a) through d) for each pixel in the input digital image that is not in a row or column at the boundary of the image.
20 . The method of claim 19 wherein:
the weight is
f
(
a
,
b
)
=
1
1
+
(
a
-
b
)
2
/
κ
(
a
,
b
)
2
;
and
κ(·) is the data-adaptive scaling function.
21 . The method of claim 20 wherein κ(·) is a function using the pixel under consideration to estimate noise variance.
22 . The method of claim 21 wherein κ(a,b)=θ(Θ 0 +Θ 1 a+Θ 2 a 2 ), where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters.
23 . The method of claim 20 wherein κ(·) is a function using the neighboring pixels to estimate noise variance.
24 . The method of claim 23 wherein κ(a,b)=θ(Θ 0 +Θ 1 b+Θ 2 b 2 ), where θ is a scaling factor, and where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters.
25 . The method of claim 21 wherein κ(·) is a function using a combination of the pixel under consideration and the neighboring pixels to estimate noise variance.
26 . The method of claim 25 wherein κ(a,b)=θ(Θ 0 +Θ 1 c+Θ 2 c 2 ), where θ is a scaling factor, and where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters, where the term c=(w a a+w b b)/(w a +w b ) and where w a and w b are weights associated with pixels a and b, respectively.
27 . A method for creating an output color digital image by performing fast detail-preserving filtering of an input color digital image comprising:
a) selecting a pixel a from the input color digital image, pixel a not being in a row or column at the boundary of the image; b) calculating separately for each color channel in the selected pixel the difference between the selected pixel and each of its four neighboring pixels located above, left, right, and below the selected pixel; c) calculating separately for each color channel in the selected pixel a scaled weighted sum of differences between the selected pixel and its four neighbors, where for each neighboring pixel b the weight is an edge-sensing function having a data-adaptive scaling parameter function; d) adding separately for each color channel in the selected pixel the weighted sum of differences to the value of the selected pixel; and e) performing a) through d) for each pixel in the input color digital image that is not in a row or column at the boundary of the image.
28 . The method of claim 27 wherein:
the weight is
f
(
a
,
b
)
=
1
1
+
(
a
-
b
)
2
/
κ
(
a
,
b
)
2
;
and
κ(·) is the data-adaptive scaling function.
29 . The method of claim 28 wherein κ(·) is a function using the pixel under consideration to estimate noise variance.
30 . The method of claim 29 wherein κ(a,b)=θ(Θ 0 +Θ 1 a+Θ 2 a 2 ), where θ is a scaling factor, and where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters
31 . The method of claim 28 wherein κ(·) is a function using the neighboring pixels to estimate noise variance.
32 . The method of claim 31 wherein κ(a,b)=θ(Θ 0 +Θ 1 b+Θ 2 b 2 ), where θ is a scaling factor, and where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters.
33 . The method of claim 28 wherein κ(·) is a function using a combination of the pixel under consideration and the neighboring pixels to estimate noise variance.
34 . The method of claim 33 wherein:
κ(a,b)=θ(Θ 0 +Θ 1 c+Θ 2 c 2 ) , where θ is a scaling factor, and where Θ 0 , Θ 1 a, and Θ 2 a 2 are quadratic model parameters, where the term c=(w a a+w b b)/(w a +w b ) and where w a and w b are weights associated with pixels a and b, respectively.
35 . The method of claim 27 wherein:
the weight
f
(
a
,
b
)
=
1
1
+
η
;
η is one of the sum or average values of (a−b) 2 /κ(a,b) 2 values obtained for each color channel; and
κ(·) is the data-adaptive scaling function.
36 . The method of claim 27 wherein:
the weight
f
(
a
,
b
)
=
1
1
+
η
1
/
η
2
;
η 1 is one of a distance or similarity measure of a−b values obtained for each color;
η 2 is one of the sum or average values of κ(a,b) 2 values obtained for each color; and
κ(·) is the data-adaptive scaling function.Join the waitlist — get patent alerts
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