CN111047521B - Infrared image non-uniformity parameterization correction optimization method based on image entropy - Google Patents

Infrared image non-uniformity parameterization correction optimization method based on image entropy Download PDF

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CN111047521B
CN111047521B CN201911065298.3A CN201911065298A CN111047521B CN 111047521 B CN111047521 B CN 111047521B CN 201911065298 A CN201911065298 A CN 201911065298A CN 111047521 B CN111047521 B CN 111047521B
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image
column
value
entropy
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CN111047521A (en
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文高进
何林
钟灿
王哲
龙亮
黄浦
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Beijing Institute of Space Research Mechanical and Electricity
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    • G06T5/90—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20216—Image averaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction

Abstract

The invention discloses an infrared non-uniformity parameterization correction optimization method based on image entropy. The specific implementation steps are as follows: 1) Calculating entropy of an original image odd pixel image and an original image even pixel image, and selecting the image with a large entropy value as a reference image; 2) And 3) performing non-uniformity parametric correction optimization processing based on image entropy on the reference image to obtain a corrected reference image, and 3) performing non-uniformity correction based on linear approximation based on the corrected reference image to finally obtain a corrected result image of the original image. The invention overcomes the defects of the traditional infrared image non-uniformity correction method and provides a more effective non-uniformity correction method based on the image entropy quality evaluation standard. The method can effectively remove the non-uniformity stripe noise in the infrared image, furthest maintain the original image information and obtain the high-quality infrared correction image.

Description

Infrared image non-uniformity parameterization correction optimization method based on image entropy
Technical Field
The invention relates to the field of infrared image processing, in particular to an infrared image non-uniformity parameter correction optimization method based on image entropy.
Background
The response transfer function of each pixel of the infrared detector is different due to the influence of the manufacturing process and materials, so that stripe noise exists in the obtained infrared image, and the stripe noise is called as non-uniformity noise. The noise seriously reduces the image quality, greatly prevents the wide application of infrared imaging in the fields of medical treatment, monitoring, agriculture and forestry and the like, and therefore, when infrared remote sensing application is carried out, non-uniformity correction is required to improve the quality of the infrared image.
The non-uniformity correction method of the infrared image mainly comprises two types: calibration-based methods and scene-based methods. Calibration-based methods require standard radiation sources, such as blackbody, which are more limited in practical applications. The scene-based method extracts parameters according to the characteristics of images in the scene to realize non-uniformity correction, has better applicability and is widely used. The scene-based correction method generally adopts the corrected image roughness as an evaluation index, which easily causes the corrected image to be excessively smooth and loses the original information contained in the image.
The image entropy represents the information quantity contained in the gray distribution aggregation feature in the image, reflects the definition of an image, and is a common quantization standard for evaluating the image.
Disclosure of Invention
The technical solution of the invention is as follows: the infrared image non-uniformity parameterization correction optimization method based on the image entropy has the advantages of large image information quantity and high definition, and overcomes the defects of the prior art.
The technical scheme of the invention is as follows: an infrared image non-uniformity parameterization correction optimization method based on image entropy comprises the following steps:
(1) Calculating the image entropy of an odd pixel image and an even pixel image of an input image X, selecting an image with a large entropy value as a reference image B, recording the entropy value e of the reference image B, and marking the other image as an image C, wherein the dimension of the image C is m rows and n2 columns; wherein m and n1, n2 are positive integers;
(2) The method comprises the following steps of:
(21) Initializing k=1;
(22) Assigning the reference image B to the process image P, initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in the process image P to obtain a column difference vector V in m dimensions, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q of all elements with absolute values smaller than U (k) in the column difference vector V, wherein the index array Q is a Q-element array, and calculating update parameters according to the following formula:
P(j,i+1)＝aP(j,i+1)+b，j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i=i+1, returning to step (23), otherwise, entering step (26);
(26) Calculating an entropy value t of the current process image P, if the entropy value t is larger than an entropy value e, updating the entropy value e into the entropy value t, and updating the current result image R into the process image P;
(27) If it isUpdating k=k+1, returning to step (22); otherwise, go to step (28);
(28) Updating the reference image B into a result graph R;
(3) The other image C carries out non-uniformity correction based on linear approximation to the reference image B, and finally obtains a corrected result, and the specific steps are as follows:
(31) Calculating an image D to be approximated from the reference image B:
if the image C is an odd pixel image of the input image X, the 1 st column of the image D is assigned as the 1 st column of the image B, and the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns at the corresponding position of the image B; otherwise, if the image C is an even pixel image of the input image X, sequentially assigning the first n-1 columns of the image D as an average image of two adjacent columns of the corresponding position of the image B, and assigning the last column of the image D as the last column of the image B;
(32) Traversing g from 1 to n, calculating a left-hand matrix F and a right-hand vector H:
F(2g,2g)＝2m，
g＝1,2,3…n；j＝1,2,3…m；
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) Calculating a linear approximation parameter t=inv (F) ·h; inv (F) is the matrix F inverted;
(34) If the image C is an odd pixel image of the input image X, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n by traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y;
otherwise, if the image C is an odd pixel image of the input image X, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y from 1 to n by traversing g:
y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
An infrared image non-uniformity parameterization correction optimization system based on image entropy, comprising:
the first module is used for calculating the image entropy of an odd pixel image and an even pixel image of an input image X, selecting an image with a large entropy value as a reference image B, recording the entropy value e of the reference image B, and recording the other image as an image C, wherein the dimension of the image C is m rows and n2 columns; wherein m and n1, n2 are positive integers;
the second module is used for carrying out non-uniformity parametric correction optimization processing based on image entropy on the reference image B to obtain a corrected reference image;
and a third module, configured to correct the non-uniformity of the other image C based on linear approximation to the reference image B, and finally obtain a corrected result.
In the second module, the specific method for obtaining the corrected reference image is as follows:
(21) Initializing k=1;
(22) Assigning the reference image B to the process image P, initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in the process image P to obtain a column difference vector V in m dimensions, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q of all elements with absolute values smaller than U (k) in the column difference vector V, wherein the index array Q is a Q-element array, and calculating update parameters according to the following formula:
P(j,i+1)＝aP(i,i+1)+b，j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i=i+1, returning to step (23), otherwise, entering step (26);
(26) Calculating an entropy value t of the current process image P, if the entropy value t is larger than an entropy value e, updating the entropy value e into the entropy value t, and updating the current result image R into the process image P;
(27) If it isUpdating k=k+1, returning to step (22); otherwise, go to step (28);
(28) The reference image B is updated to the result map R.
In the third module, the non-uniformity correction based on linear approximation is performed on the other image C to the reference image B, and the specific method for obtaining the correction result is as follows:
(31) Calculating an image D to be approximated from the reference image B:
if the image C is an odd pixel image of the input image X, the 1 st column of the image D is assigned as the 1 st column of the image B, and the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns at the corresponding position of the image B; otherwise, if the image C is an even pixel image of the input image X, sequentially assigning the first n-1 columns of the image D as an average image of two adjacent columns of the corresponding position of the image B, and assigning the last column of the image D as the last column of the image B;
(32) Traversing g from 1 to n, calculating a left-hand matrix F and a right-hand vector H:
F(2g,2g)＝2m，
g＝1,2,3…n；j＝1,2,3…m；
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) Calculating a linear approximation parameter t=inv (F) ·h; inv (F) is the matrix F inverted;
(34) If the image C is an odd pixel image of the input image X, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n by traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y;
otherwise, if the image C is an odd pixel image of the input image X, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y from 1 to n by traversing g:
y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
Compared with the prior art, the invention has the advantages that:
(1) The method evaluates the quality of the infrared image after the infrared non-uniformity correction from the angle of image entropy, avoids the problem that the quality evaluation of the traditional infrared image non-uniformity correction method depends on a roughness index and cannot represent the limitation of the information quantity of the image;
(2) The method provides a selection mechanism of the infrared image non-uniformity parameterization correction method, so that the existing infrared image non-uniformity parameterization correction method is easily expanded, and the infrared image non-uniformity correction method with better performance is conveniently generated;
(3) The method effectively combines the information of the odd pixel image and the even pixel image of the original infrared image to carry out the non-uniformity correction method, and compared with the traditional infrared image non-uniformity correction method, the method has better infrared image correction effect obviously influenced by odd-even non-uniformity;
(4) The method ensures the maximization of the image information while correcting the non-uniformity of the infrared image, and the corrected infrared image has higher definition and target recognition.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an infrared image 269X 384 to be corrected for non-uniformity;
FIG. 3 is an entropy curve of a process image corresponding to different K values;
fig. 4 is a graph of the result of the non-uniformity correction of an example infrared image.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Taking 269×384 infrared images to be corrected for non-uniformity as an example, a flow of an infrared image non-uniformity parameterization correction preferred method based on image entropy is shown in fig. 1, and the specific process is as follows:
(1) The image entropy of the input image X odd pixel image and the image of the even pixel image is calculated, an image with a large entropy value is selected as a reference image B (m rows and n1 columns), the entropy value e is recorded, and the other image is marked as C (m rows and n2 columns). The image entropy can be calculated by selecting one-dimensional entropy or two-dimensional entropy, wherein m and n1, n2 are positive integers.
In this embodiment, as shown in fig. 2, the input infrared image X to be corrected for non-uniformity is a 220×320 matrix, the odd pixel image is X odd, m=269 row, n1=192 column, the corresponding pixel column is X1, 3,5,..383 column, the even image is m=269 row, n2=192 column, and the corresponding pixel column is X2, 4,6,..384 column. The image entropy adopts a calculation formula of one-dimensional entropy, namely, the image entropy of X odd is 5.8094, and the image entropy of X even is 5.9658, so that the reference image B is an odd image, the other image C is an even image, and the image entropy of original image X is 6.1223.
(2) Performing non-uniformity parameterization correction based on image entropy on the reference image B to obtain a corrected reference image, and specifically comprising the following steps:
(21) Traversing parameter k from 1 toPerforming the following steps, wherein the symbol representation takes the largest integer not greater than b;
(22) Assigning the reference image B to the process image P, traversing the image P according to the columns, and initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in P to obtain a column difference vector V in m dimension, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q (Q-element array) of all elements with absolute values smaller than U (k) in V, and calculating update parameters according to the following formula:
P(j,i+1)＝aP(j,i+1)+b,j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i=i+1, returning to step (23), otherwise proceeding to step (26)
(26) Calculating the entropy value t of the current process image P, if t is larger than e, updating e to t, and updating the current result image R to P;
(27) If it isUpdating k=k+1, returning to step (22), otherwise proceeding to step (28)
Fig. 3 is an entropy curve of a process image corresponding to different K values, where the maximum entropy is 6.5362, appearing at a position of k=82.
(28) Updating the reference image B into a result graph R;
(3) The other image C carries out non-uniformity correction based on linear approximation to the reference image B, and finally obtains a corrected result, and the specific steps are as follows:
(31) Calculating from the reference image B to obtain an image D to be approximated
If the image C is an odd pixel image of the original image, the 1 st column of the image D is assigned as the 1 st column of the image B, the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns of the corresponding position of the image B, otherwise, the previous n-1 columns of the image D are sequentially assigned as average images of two adjacent columns of the corresponding position of the image B, and the last column of the image D is the last column of the image B.
(32) The left matrix F and the right vector H are calculated by traversing g from 1 to n by:
F(2g,2g)＝2m，
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) The linear approximation parameter t=inv (F) H is calculated,
(34) If the image C is an odd pixel image of the original image, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n through traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y;
otherwise, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y by traversing g from 1 to n in the following steps
Y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
The image Y calculated in this embodiment is shown in fig. 4, and the image entropy of the result graph is 6.6785 and the roughness index 0.0862.
An infrared image non-uniformity parameterization correction optimization system based on image entropy, comprising:
the first module is used for calculating the image entropy of an odd pixel image and an even pixel image of an input image X, selecting an image with a large entropy value as a reference image B, recording the entropy value e of the reference image B, and recording the other image as an image C, wherein the dimension of the image C is m rows and n2 columns; wherein m and n1, n2 are positive integers;
the second module is used for carrying out non-uniformity parametric correction optimization processing based on image entropy on the reference image B to obtain a corrected reference image;
and a third module, configured to correct the non-uniformity of the other image C based on linear approximation to the reference image B, and finally obtain a corrected result.
In the second module, the specific method for obtaining the corrected reference image is as follows:
(21) Initializing k=1;
(22) Assigning the reference image B to the process image P, initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in the process image P to obtain a column difference vector V in m dimensions, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q of all elements with absolute values smaller than U (k) in the column difference vector V, wherein the index array Q is a Q-element array, and calculating update parameters according to the following formula:
P(j,i+1)＝aP(j,i+1)+b，j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i=i+1, returning to step (23), otherwise, entering step (26);
(26) Calculating an entropy value t of the current process image P, if the entropy value t is larger than an entropy value e, updating the entropy value e into the entropy value t, and updating the current result image R into the process image P;
(27) If it isUpdating k=k+1, returning to step (22); otherwise, go to step (28);
(28) The reference image B is updated to the result map R.
In the third module, the non-uniformity correction based on linear approximation is performed on the other image C to the reference image B, and the specific method for obtaining the correction result is as follows:
(31) Calculating an image D to be approximated from the reference image B:
if the image C is an odd pixel image of the input image X, the 1 st column of the image D is assigned as the 1 st column of the image B, and the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns at the corresponding position of the image B; otherwise, if the image C is an even pixel image of the input image X, sequentially assigning the first n-1 columns of the image D as an average image of two adjacent columns of the corresponding position of the image B, and assigning the last column of the image D as the last column of the image B;
(32) Traversing g from 1 to n, calculating a left-hand matrix F and a right-hand vector H:
F(2g，2g)＝2m，
g＝1,2,3…n；j＝1,2,3…m；
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) Calculating a linear approximation parameter t=inv (F) ·h; inv (F) is the matrix F inverted;
(34) If the image C is an odd pixel image of the input image X, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n by traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y;
otherwise, if the image C is an odd pixel image of the input image X, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y from 1 to n by traversing g:
y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
The invention, in part not described in detail, is within the skill of those skilled in the art.

Claims (2)

1. An infrared image non-uniformity parameterization correction optimization method based on image entropy is characterized by comprising the following steps:
(1) Calculating the image entropy of an odd pixel image and an even pixel image of an input image X, selecting an image with a large entropy value as a reference image B, recording the entropy value e of the reference image B, and marking the other image as an image C, wherein the dimension of the image C is m rows and n2 columns; wherein m and n1, n2 are positive integers;
(2) Performing non-uniformity parameterization correction optimization processing on the reference image B based on image entropy to obtain a corrected reference image;
(3) Performing non-uniformity correction based on linear approximation on the other image C to the reference image B, and finally obtaining a corrected result;
the specific steps of the step (2) are as follows:
(21) Initializing k=1;
(22) Assigning the reference image B to the process image P, initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in the process image P to obtain a column difference vector V in m dimensions, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q of all elements with absolute values smaller than U (k) in the column difference vector V, wherein the index array Q is a Q-element array, and calculating update parameters according to the following formula:
P(j，i+1)＝aP(j，i+1)+b，j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i = i+1, returning to step (23), otherwise, entering step (26);
(26) Calculating an entropy value t of the current process image P, if the entropy value t is larger than an entropy value e, updating the entropy value e into the entropy value t, and updating the current result image R into the process image P;
(27) If it isUpdating k=k+1, returning to step (22); otherwise, go to step (28);
(28) Updating the reference image B into a result graph R;
the specific steps of the step (3) are as follows:
(31) Calculating an image D to be approximated from the reference image B:
if the image C is an odd pixel image of the input image X, the 1 st column of the image D is assigned as the 1 st column of the image B, and the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns at the corresponding position of the image B; otherwise, if the image C is an even pixel image of the input image X, sequentially assigning the first n-1 columns of the image D as an average image of two adjacent columns of the corresponding position of the image B, and assigning the last column of the image D as the last column of the image B;
(32) Traversing g from 1 to n, calculating a left-hand matrix F and a right-hand vector H:
F(2g，2g)＝2m，
g＝1,2,3…n；j＝1,2,3…m；
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) Calculating a linear approximation parameter t=inv (F) ·h; inv (F) is the matrix F inverted;
(34) If the image C is an odd pixel image of the input image X, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n by traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y;
otherwise, if the image C is an odd pixel image of the input image X, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y from 1 to n by traversing g:
y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
2. An infrared image non-uniformity parameterized correction optimization system based on image entropy, comprising:
the first module is used for calculating the image entropy of an odd pixel image and an even pixel image of an input image X, selecting an image with a large entropy value as a reference image B, recording the entropy value e of the reference image B, and recording the other image as an image C, wherein the dimension of the image C is m rows and n2 columns; wherein m and n1, n2 are positive integers;
the second module is used for carrying out non-uniformity parametric correction optimization processing based on image entropy on the reference image B to obtain a corrected reference image;
a third module, configured to perform non-uniformity correction based on linear approximation on the other image C to the reference image B, and finally obtain a corrected result;
in the second module, the specific method for obtaining the corrected reference image is as follows:
(21) Initializing k=1;
(22) Assigning the reference image B to the process image P, initializing i=1;
(23) Calculating the gray value difference of the corresponding pixels in the (i+1) th column and the (i) th column in the process image P to obtain a column difference vector V in m dimensions, and sorting the absolute values of all elements of the column difference vector V from small to large to obtain a sorted absolute value vector U;
(24) Calculating to obtain an index array Q of all elements with absolute values smaller than U (k) in the column difference vector V, wherein the index array Q is a Q-element array, and calculating update parameters according to the following formula:
P(j，i+1)＝aP(j，i+1)+b，j＝1,2,3,...,m；
u (k) is the kth element in the absolute value vector U;
r 1 、r 2 、r 3 、r 4 intermediate amounts respectively;
p (x, y) is the value of the x-th row, y-th column in the process image P;
a. b is a correction coefficient respectively; q (j) is the j-th element in the index array Q;
(25) If i < = n-1, updating i = i+1, returning to step (23), otherwise, entering step (26);
(26) Calculating an entropy value t of the current process image P, if the entropy value t is larger than an entropy value e, updating the entropy value e into the entropy value t, and updating the current result image R into the process image P;
(27) If it isUpdating k=k+1, returning to step (22); otherwise, go to step (28);
(28) Updating the reference image B into a result graph R;
in the third module, the non-uniformity correction based on linear approximation is performed on the other image C to the reference image B, and the specific method for obtaining the correction result is as follows:
(31) Calculating an image D to be approximated from the reference image B:
if the image C is an odd pixel image of the input image X, the 1 st column of the image D is assigned as the 1 st column of the image B, and the subsequent n-1 columns of the image D are sequentially assigned as average images of two adjacent columns at the corresponding position of the image B; otherwise, if the image C is an even pixel image of the input image X, sequentially assigning the first n-1 columns of the image D as an average image of two adjacent columns of the corresponding position of the image B, and assigning the last column of the image D as the last column of the image B;
(32) Traversing g from 1 to n, calculating a left-hand matrix F and a right-hand vector H:
F(2g，2g)＝2m，
f (x, y) is the value of the x-th row and y-th column in the left matrix F;
c (x, y) is the value of the x-th row and y-th column in the image C;
h (x) is the x-th element in the right-end vector H;
d (x, y) is the value of the x-th row, y-th column in the image D;
(33) Calculating a linear approximation parameter t=inv (F) ·h; inv (F) is the matrix F inverted;
(34) If the image C is an odd pixel image of the input image X, setting the even pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the odd pixel of the image Y from 1 to n by traversing g:
y (h, 2 g-1) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
t (2 g-1) and T (2 g) are respectively the 2g-1 element and the 2g element in the matrix T; y (x, Y) is the value of the x-th row and Y-th column in the image Y; otherwise, if the image C is an odd pixel image of the input image X, setting the odd pixel gray level of the image Y as the pixel gray level corresponding to the reference image B, and updating the even pixel of the image Y from 1 to n by traversing g:
y (h, 2 g) =t (2 g-1) C (h, g) +t (2 g), wherein h=1, 2,3,..m;
image Y is the final corrected image.
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