Image denoising method and image denoising apparatus
The present invention provides an image denoising method and an image denoising apparatus. The image denoising method includes performing preliminary denoising processing to an acquired image to be processed, so as to obtain a preliminarily denoised image; calculating a residual quantity corresponding to a central pixel of each unit area in the image to be processed according to numerical values of specific energy parameters to which the image to be processed and the preliminarily denoised image correspond, respectively; and using the residual quantity to calculate a weight matrix corresponding to each unit area, and performing non-local mean value calculation to the image to be processed according to the weight matrix, so as to realize the denoising processing of the image to be processed. The image denoising method is able to denoise effectively, and make a denoised image more visually natural.
1. An image denoising method, comprising:
performing preliminary denoising processing of an acquired image to be processed, so as to obtain a preliminarily denoised image;
calculating a residual quantity corresponding to a central pixel of each unit area in the image to be processed according to numerical values of specific energy parameters to which the image to be processed and the preliminarily denoised image correspond, respectively, the residual quantity is an absolute value of a difference between a gray scale value of the acquired image to be processed and a gray scale value of the preliminarily denoised image; and
using the residual quantity to calculate a weight matrix corresponding to each unit area, and performing a non-local mean value calculation on the image to be processed according to the weight matrix to realize the denoising processing of the image to be processed.
2. The image denoising method according to claim 1 , wherein the weight matrix calculation comprises:
selecting an arbitrary unit area in the image to be processed, and determining an associated area of the arbitrary unit area in the image to be processed; and
calculating a weight value corresponding to each associated unit area according to a value of a distance between each associated unit area in the associated area and the arbitrary unit area and the residual quantity to obtain the weight matrix.
3. The image denoising method according to claim 2 , wherein the weight value corresponding to an arbitrary associated unit area in the associated area is calculated by:
w ( n,m ) =e (−(d(n,m)+residuals(n,m))/h) ;
where, w(n, m) is the weight value to which an arbitrary associated unit area (n, m) corresponds, d(n, m) is the value of the distance between the arbitrary associated unit area and the arbitrary unit area, residuals(n, m) is the residual quantity corresponding to the central pixel of the arbitrary unit area, h is a predefined control coefficient.
4. The image denoising method according to claim 3 , wherein the value of the distance between the arbitrary associated unit area (n, m) and the arbitrary unit area (i, j) is calculated by:
d
(
n
,
m
)
=
∑
t
=
-
r
r
∑
k
=
-
r
r
(
(
x
(
i
+
k
,
j
+
t
)
-
x
(
n
+
k
,
m
+
t
)
)
2
/
(
(
k
2
+
t
2
)
1.25
+
0.5
T
)
)
,
T
=
∑
t
=
-
r
r
∑
k
=
-
r
r
(
(
k
2
+
t
2
)
1.25
+
0.5
)
;
where, 2r +1 is a side length of the arbitrary associated unit area, and l, k and T are intermediate values.
5. The image denoising method according to claim 2 , wherein the non-local mean value calculation comprises:
calculating a weighted sum of all pixels in the associated area according to a relationship between each associated unit area in the associated area and a weight value in the weight matrix as a result of denoising the central pixel of the arbitrary unit area.
6. An image denoising apparatus, comprising:
a memory that stores a plurality of instructions; and
a processor coupled to the memory and configured to execute the instructions to:
perform preliminary denoising processing of an acquired image to be processed to obtain a preliminarily denoised image;
calculate a residual quantity corresponding to a central pixel of each unit area in the image to be processed according to numerical values of specific energy parameters to which the image to be processed and the preliminarily denoised image correspond, respectively, the residual quantity is an absolute value of a difference between a gray scale value of the acquired image to be processed and a gray scale value of the preliminarily denoised image; and
use the residual quantity to calculate a weight matrix corresponding to each unit area, and perform a non-local mean value calculation on the image to be processed according to the weight matrix to realize the denoising processing of the image to be processed.
7. The image denoising apparatus according to claim 6 , wherein the processor is further configured to:
select an arbitrary unit area in the image to be processed, and determine an associated area of the arbitrary unit area in the image to be processed; and
calculate a weight value corresponding to each associated unit area according to a value of a distance between each associated unit area in the associated area and the arbitrary unit area and the residual quantity to obtain the weight matrix.
8. The image denoising apparatus according to claim 7 , wherein the processor is further configured to uses a formula to calculate the weight value corresponding to an arbitrary associated unit area in the associated area, the formula comprising:
w ( n,m ) =e (−(d(n,m)+residuals(n,m))/h) ;
where, w(n, m) is the weight value to which an arbitrary associated unit area (n, m) corresponds, d(n, m) is the value of the distance between the arbitrary associated unit area and the arbitrary unit area, residuals(n, m) is the residual quantity corresponding to the central pixel of the arbitrary unit area, h is a predefined control coefficient.
9. The image denoising apparatus according to claim 8 , wherein the value of the distance between the arbitrary associated unit area (n, m) and the arbitrary unit area (i, j) is calculated by:
d
(
n
,
m
)
=
∑
t
=
-
r
r
∑
k
=
-
r
r
(
(
x
(
i
+
k
,
j
+
t
)
-
x
(
n
+
k
,
m
+
t
)
)
2
/
(
(
k
2
+
t
2
)
1.25
+
0.5
T
)
)
,
T
=
∑
t
=
-
r
r
∑
k
=
-
r
r
(
(
k
2
+
t
2
)
1.25
+
0.5
)
;
where, 2r +1 is a side length of the arbitrary associated unit area, and l, k and T are intermediate values.
10. The image denoising apparatus according to claim 7 , wherein the processor is further configured to:
calculate a weighted sum of all pixels in the associated area according to a relationship between each associated unit area in the associated area and a weight value in the weight matrix as a result of denoising the central pixel of the arbitrary unit area.