CN113658054B - Infrared image stitching correction method based on temperature drift characteristic line approximation - Google Patents
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Abstract
The invention relates to an infrared image stitching correction method based on temperature drift characteristic line approximation, belonging to the field of infrared image processing; the invention realizes the simultaneous temperature drift characteristic calculation and the non-uniformity correction treatment in the infrared image splicing treatment, overcomes the defects of the traditional infrared image splicing method, provides a multi-scale approximation method of the relative temperature drift characteristic curve of the infrared image, forms a more stable and effective infrared image splicing method, can effectively remove the temperature drift noise in the infrared image, furthest maintains the high-frequency information of the image, and obtains the high-quality infrared splicing result image.
Description
Technical Field
The invention belongs to the field of infrared image processing, and relates to an infrared image stitching correction method based on temperature drift characteristic line approximation.
Background
On the one hand, 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, called non-uniformity noise, exists in the obtained infrared image. On the other hand, the temperature of the infrared detector is continuously changed under the influence of the ambient temperature, the target thermal radiation and the like, so that temperature drift phenomena with different degrees are formed, the spectral responsivity of the detector is changed, the output voltage of the detector is changed, and the repeatability and the accuracy of a measurement result are affected. When the detectors work cooperatively, serious inconsistency of formed images is caused, and difficulties are brought to infrared image stitching. The two factors seriously reduce the image quality, greatly prevent the wide application of infrared imaging in the fields of medical treatment, monitoring, agriculture and forestry and the like, and therefore, when the infrared remote sensing application of the combination of a plurality of detectors is developed, splicing correction based on temperature drift treatment is needed to improve the quality of the infrared image.
The infrared image splicing method mainly comprises two methods, namely a radiation thermometer calibration method and an inter-chip non-uniformity correction method. According to the radiation thermometer calibration method, the radiation thermometer is adopted to measure the temperature of the detector in real time, a temperature drift curve of spectral responsivity is obtained, a fitting equation of the temperature drift curve is obtained through data fitting, a functional relation between the temperature of the detector and the spectral responsivity and a temperature drift correction coefficient are determined, and the correction of the spectral responsivity temperature drift of the detector is realized. The method needs to be provided with a radiation thermometer for each infrared detector, and has difficulty in structural design and reliability design of a detection system, and the method is limited in practical application. The inter-chip non-uniformity correction method generally adopts a two-point or multi-point non-uniformity correction method to perform correction processing. The two methods can not effectively process the nonlinear temperature drift problem of the multiple detectors, and the corrected images still have the chromatic aberration problem.
Disclosure of Invention
The invention solves the technical problems that: the infrared image stitching correction method based on the temperature drift characteristic line approximation is provided, temperature drift noise in an infrared image can be effectively removed, high-frequency information of the image is kept to the maximum extent, and a high-quality infrared stitching result image is obtained.
The solution of the invention is as follows:
an infrared image stitching correction method based on temperature drift characteristic line approximation comprises the following steps:
step one, setting an initialization scale parameter d and a correction error e of a first iteration 1 ；
Step two, inputting an infrared image X of m rows and n columns and an infrared image Y of s rows and n columns, and calculating an expansion image of the infrared image XCalculating an extended image of the infrared image Y>
Step three, calculating a multi-scale filter H and a temperature drift initial curve V;
step four, according to the expanded imageExpansion image->The multiscale filter H and the temperature drift initial curve V are subjected to approximation correction optimization processing to obtain a final correction error e d ；
Step five, pair e d Make a judgment when e d ＜e d-1 At the time, the temperature drift curve of the d-th iterationMarked as temperature drift optimization curve->And the d value is marked as the optimal scale +.>And entering a step six; when e d ≥e d-1 When the method is used, directly entering the step six;
step six, setting a threshold D, comparing the value D with the threshold D, and entering a step seven when D is more than D; otherwise, returning to the second step;
step seven, optimizing curve e according to temperature drift d ＜e d-1 Optimum dimensionsPerforming approximation stitching treatment on the infrared image X and the infrared image Y to obtain a stitched result diagram +.>
In the above infrared image stitching correction method based on temperature drift characteristic line approximation, in the first step, d=2, e 1 ＝1。
In the above-mentioned infrared image stitching correction method based on temperature drift characteristic line approximation, in the second step,for an m row n+2d-2 column matrix; />For an s-row n+2d-2 column matrix; wherein, expand the picture +.>The iterative formula of (2) satisfies:
wherein j=1, 2, … …, m; t=1, 2, … …, d-1;
wherein j=1, 2, …, m; t=1, 2, … …, d-1;
wherein j=1, 2, … …, m; t=1, 2, … …, n;
expanding imagesThe iterative formula of (2) satisfies:
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, n.
In the above-mentioned infrared image stitching correction method based on temperature drift characteristic line approximation, in the third step, the multi-scale filter H is a 2d-1 dimensional vector, specifically:
H＝[12……d*(d-1)……1]/(d+d*(d-1))
the initial curve V of temperature drift is:
V＝[V 1 V 2 …V n+2d-2 ]
wherein X is k,p Values for the kth row and the p column in the matrix X;
Y k,p values for the kth row and the p column in the matrix Y;
for matrix->The value of the kth row, p column;
for matrix->The value of the kth row, p column;
H k is the kth element in the vector H;
V k is the kth element in vector V;
in the above-mentioned infrared image stitching correction method based on temperature drift characteristic line approximation, in the fourth step, the specific method of approximation correction optimization processing is as follows:
s41, setting the iteration number as a, and setting the correction error r of the 0 th iteration 0 1 is shown in the specification;
s42, updating and calculating a temperature drift filter parameter matrix U of the a-th row and the n+2d-2 th column;
s43, calculating an approximation graph Z of s+1 rows and n columns and a correction error r of the a-th iteration a ；
S44, correcting error r of a-th iteration a Is judged by the iteration value of (1), when r a ＜r a-1 When the temperature drift optimization curve under the current scale parameter d is adoptedUpdating the element to be the a-th row element of the temperature drift filtering parameter matrix U, and adding r to the element a Recorded as final correction error e d S45 is entered; when r is a ≥r a-1 When the updating is not performed, the process directly goes to S45;
s45, pair |r a -r a -1| making the determination when |r a -r a -1|＜1.0×10 -5 Step five is carried out when the process is carried out; if not, a=a+1, and the process returns to S42.
In the above-mentioned method for correcting the infrared image stitching based on the approximation of the temperature drift characteristic line, in S42, the method for calculating the temperature drift filtering parameter matrix U is as follows:
when a=1, initializing to let U of the 1 st row and j th column of the matrix U 1,j ＝V j ，j＝1，2，……，n+2d-2；
Updating U 1,j The calculation formula of (2) is as follows:where j=d, d+1, … …, d+n-1;
in U k,p Values of the kth row and the p column in the matrix U;
H k is the kth element in vector H;
V k is the kth element in vector V;
when a is more than 1, initializing to make U a,j ＝U a-1,j ，j＝1，2，……，n+2d-2；
Updating U a,j The calculation formula of (2) is as follows:
in the above method for infrared image stitching correction based on temperature drift characteristic line approximation, in S43, the approximation graph Z satisfies:
Z 1,j ＝X m,j wherein j=1, 2, … …, n
Wherein t=1, 2, … …, s; j=1, 2, … …, n;
correction error r for the a-th iteration a The method comprises the following steps:
wherein Z is k,p Is the value of the kth row, p column in the matrix Z.
In the above infrared image stitching correction method based on temperature drift characteristic line approximation, in the sixth step, the value range of the threshold D is: d is more than or equal to 12 and less than or equal to 16.
In the above infrared image stitching correction method based on temperature drift characteristic line approximation, in the seventh step, a stitched result graphThe method meets the following conditions:
wherein i=1, 2, … …, m; j=1, 2, … …, n;
wherein i=1, 2, … …, s; j=1, 2, … …, n;
in the method, in the process of the invention,for matrix->The value of the kth row, p column.
Compared with the prior art, the invention has the beneficial effects that:
(1) The nonlinear temperature drift characteristic line is approximated in a multi-scale filter mode, so that the accuracy and stability of an approximation result can be effectively ensured;
(2) According to the invention, the non-uniformity correction objective function is organically integrated into the infrared image splicing processing, so that the multi-scale temperature drift characteristic approximation calculation and the non-uniformity correction processing can be simultaneously carried out;
(3) The invention can effectively remove temperature drift noise in the infrared image, furthest maintain image high-frequency information, obtain high-quality infrared splicing result images, and have higher definition and recognition degree of targets.
Drawings
FIG. 1 is a flow chart of the infrared image stitching correction of the present invention;
FIG. 2 is an initial stitching diagram of infrared images X and Y to be stitched according to the present invention;
FIG. 3 is a graph of an initial temperature drift in accordance with the present invention;
FIG. 4 is a graph of temperature drift after optimization in accordance with the present invention;
FIG. 5 is a graph of the result of the infrared image stitching of the present invention.
Detailed Description
The invention is further illustrated below with reference to examples.
The invention provides an infrared image stitching correction method based on temperature drift characteristic line approximation, which can effectively ensure global optimality and stability of stitching results and can keep better image high-frequency information performance to the maximum extent.
As shown in FIG. 1, the infrared image stitching correction method specifically comprises the following steps:
step one, setting an initialization scale parameter d as 2, and enabling a correction error e of a first iteration 1 1.
Step two, inputting an infrared image X of m rows and n columns and an infrared image Y of s rows and n columns, as shown in fig. 2. Extended image for calculating infrared image XCalculating an extended image of the infrared image Y>For an m row n+2d-2 column matrix; />For an s-row n+2d-2 column matrix; wherein, expand the picture +.>The iterative formula of (2) satisfies:
wherein j=1, 2, … …, m; t=1, 2, … …, d-1;
wherein j=1, 2, …, m; t=1, 2, … …, d-1;
wherein j=1, 2, … …, m; t=1, 2, … …, n;
expanding imagesThe iterative formula of (2) satisfies:
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, n.
Step three, calculating a multi-scale filter H and a temperature drift initial curve V; the multi-scale filter H is a 2d-1 dimensional vector, and specifically comprises the following components:
H＝[12……d*(d-1)……1]/(d+d*(d-1))
the initial curve V of temperature drift is:
V＝[V 1 V 2 …V n+2d-2 ]
wherein X is k,p Values for the kth row and the p column in the matrix X;
Y k,p values for the kth row and the p column in the matrix Y;
for matrix->The value of the kth row, p column;
for matrix->The value of the kth row, p column;
H k is the kth element in the vector H;
V k is the kth element in vector V;
the initial curve V of temperature drift is shown in FIG. 3.
Step four, according to the expanded imageExpansion image->The multiscale filter H and the temperature drift initial curve V are subjected to approximation correction optimization processing to obtain a final correction error e d The method comprises the steps of carrying out a first treatment on the surface of the The specific method for the approximation correction optimization processing comprises the following steps:
s41, setting the iteration number as a, and setting the correction error r of the 0 th iteration 0 1.
S42, updating and calculating a temperature drift filter parameter matrix U of the a-th row and the n+2d-2 th column; the calculation method of the temperature drift filtering parameter matrix U comprises the following steps:
when a=1, initializing to let U of the 1 st row and j th column of the matrix U 1,j ＝V j ，j＝1，2，……，n+2d-2；
Updating U 1,j The calculation formula of (2) is as follows:where j=d, d+1, … …, d+n-1;
in U k,p Values of the kth row and the p column in the matrix U;
H k is the kth element in vector H;
V k is the kth element in vector V;
when a is more than 1, initializing to make U a,j ＝U a-1,j ，j＝1，2，……，n+2d-2；
Updating U a,j The calculation formula of (2) is as follows:s43, calculating an approximation graph Z of s+1 rows and n columns and a correction error r of the a-th iteration a The method comprises the steps of carrying out a first treatment on the surface of the The approximation graph Z satisfies:
Z 1,j ＝X m,j wherein j=1, 2, … …, n
Wherein t=1, 2, … …, s; j=1, 2, … …, n;
correction error r for the a-th iteration a The method comprises the following steps:
wherein Z is k,p Is the value of the kth row, p column in the matrix Z.
S44, correcting error r of a-th iteration a Is judged by the iteration value of (1), when r a ＜r a-1 When the temperature drift optimization curve under the current scale parameter d is adoptedUpdating the element to be the a-th row element of the temperature drift filtering parameter matrix U, and adding r to the element a Recorded as final correction error e d S45 is entered; when r is a ≥r a-1 When this is the case, the process proceeds directly to S45 without updating.
S45, pair |r a -r a -1| making the determination when |r a -r a -1|＜1.0×10 -5 Step five is carried out when the process is carried out; if not, a=a+1, and the process returns to S42.
Step five, pair e d Make a judgment when e d ＜e d-1 At the time, the temperature drift curve of the d-th iterationMarked as temperature drift optimization curve->And the d value is marked as the optimal scale +.>And entering a step six; when e d ≥e d-1 And (3) directly entering a step six.
Step six, setting a threshold value D, wherein the value range of the threshold value D is as follows: d is more than or equal to 12 and less than or equal to 16. Comparing the D value with a threshold D, and entering a step seven when D is more than D; otherwise, returning to the second step;
step seven, optimizing curve e according to temperature drift d ＜e d-1 Optimum dimensionsPerforming approximation stitching treatment on the infrared image X and the infrared image Y to obtain a stitched result diagram +.>As shown in fig. 5. Spliced result diagram->The method meets the following conditions:
wherein i=1, 2, … …, m; j=1, 2, … …, n;
wherein i=1, 2, … …, s; j=1, 2, … …, n;
in the method, in the process of the invention,for matrix->The value of the kth row, p column.
Further description of specific embodiments follows:
taking the 12-bit infrared image X of 528×4101 to be spliced and the 12-bit infrared image Y of 545×4101 as examples, the flow of the infrared image stitching correction method based on the temperature drift characteristic line approximation is shown in fig. 1, and the specific process is as follows:
(1) Setting an initialization scale parameter d=2, and correcting an error e 1 ＝1；
(2) Calculating an expansion image according to the input m-row n-column infrared image X and the input s-row n-column infrared image YAnd->Calculating a multi-scale filter H and a temperature drift initial curve V; in this embodiment, as shown in fig. 2, the input infrared image X to be spliced is a 528×4101 matrix, and the spliced infrared image Y is a 545×4101 matrix;
wherein j=1, 2, … …, m; t=1, 2, … …, d-1;
wherein j=1, 2, …, m; t=1, 2, … …, d-1;
wherein j=1, 2, … …, m; t=1, 2, … …, n;
expanding imagesThe iterative formula of (2) satisfies:
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, n;
h is a 2d-1 dimensional vector, h= [12 … … d (d-1) … … 1 ]/(d+d (d-1));
V＝[V 1 V 2 …V n+2d-2 ]，
the initial temperature drift curve V is shown in fig. 3.
(3) From extended imagesAnd->The multiscale filter H and the temperature drift initial curve V are subjected to approximation correction optimization processing to obtain a final correction error e d The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following specific steps:
(3-1) setting the number of iterations a=1, setting the correction error r of the 0 th iteration 0 1 is shown in the specification;
(3-2) updating and calculating a temperature drift filter parameter matrix U of the a line and the n+2d-2 column;
when a=1, initializing to let U of the 1 st row and j th column of the matrix U 1,j ＝V j ，j＝1，2，……，n+2d-2；
Then updating the calculation formula as follows:
wherein: u (U) k,p Values of the kth row and the p column in the matrix U;
H k is the kth element in vector H;
V k is the kth element in vector V;
if a is>1, first initialize U a,j ＝U a-1,j ，j＝1，2，…,n+2d-2；
Then updating the calculation formula as follows:
(3-3) calculating the correction error r of the s+1 row n column approximation map Z and the a-th iteration a ；
Z 1,j ＝X m,j Where j=1, 2, …, n;
wherein t=1, 2, …, s; j=1, 2, …, n;
wherein: z is Z k,p Is the value of the kth row, p column in the matrix Z.
(3-4) if r a ＜r a-1 Optimizing curve of temperature drift under current scale parameter dUpdating the element to be the a-th row element of the temperature drift filtering parameter matrix U, and adding r to the element a Recorded as final correction error e d Enter (3-5); when r is a ≥r a-1 When the update is not performed, the process directly enters (3-5).
(3-5) when |r a -r a -1|＜1.0×10 -5 If so, the step (4) is entered, otherwise, a=a+1 is caused, and the step (3-2) is returned.
(4) If e d ＜e d-1 Temperature drift curve of the d-th iterationMarked as temperature drift optimization curve->And the d value is marked as the optimal scale +.>And enter step (5); when e d ≥e d-1 And (5) directly entering the step (5).
(5) Setting a threshold D, if the D is more than or equal to 12 and less than or equal to 16, entering a step (6), otherwise, enabling d=d+1, and returning to the step (2). The resulting optimized temperature drift curve is shown in FIG. 4.
(6) Temperature drift optimization curve obtained by optimizationAnd optimal dimension->Performing approximation stitching treatment on the input infrared images X and Y to obtain a stitched result diagram +.>A graph of the result of the example non-uniformity correction of the infrared image is shown in fig. 5.
According to the infrared image stitching correction method based on temperature drift characteristic line approximation, the nonlinear temperature drift characteristic line is approximated in the form of the multi-scale filter, accuracy and stability of approximation results can be effectively guaranteed, meanwhile, a non-uniformity correction objective function is organically integrated into infrared image stitching processing, multi-scale temperature drift characteristic approximation calculation and non-uniformity correction processing can be simultaneously carried out, temperature drift noise in an infrared image can be effectively removed, image high-frequency information is kept to the maximum, high-quality infrared stitching result images are obtained, and definition and target recognition degree of targets are higher.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
Claims (8)
1. An infrared image stitching correction method based on temperature drift characteristic line approximation is characterized in that: the method comprises the following steps:
step one, setting an initialization scale parameter d and a correction error e of a first iteration 1 ；
Step two, inputting an infrared image X of m rows and n columns and an infrared image Y of s rows and n columns, and calculating an expansion image of the infrared image XCalculating an extended image of the infrared image Y>
Step three, calculating a multi-scale filter H and a temperature drift initial curve V;
step four, according to the expanded imageExpansion image->The multiscale filter H and the temperature drift initial curve V are subjected to approximation correction optimization processing to obtain a final correction error e d ；
In the fourth step, the specific method for the approximation correction optimization process comprises the following steps:
s41, setting the iteration number as a, and setting the correction error r of the 0 th iteration 0 1 is shown in the specification;
s42, updating and calculating a temperature drift filter parameter matrix U of the a-th row and the n+2d-2 th column;
s43, calculating an approximation graph Z of s+1 rows and n columns and a correction error r of the a-th iteration a ；
S44, correcting error r of a-th iteration a Is judged by the iteration value of (1), when r a ＜r a-1 When the temperature drift optimization curve under the current scale parameter d is adoptedUpdating the element to be the a-th row element of the temperature drift filtering parameter matrix U, and adding r to the element a Recorded as final correction error e d S45 is entered; when r is a ≥r a-1 When the updating is not performed, the process directly goes to S45;
s45, pair |r a -r a-1 Judging when |r a -r a-1 |＜1.0×10 -5 Step five is carried out when the process is carried out; if not, a=a+1, and the process returns to S42;
step five, pair e d Make a judgment when e d ＜e d-1 At the time, the temperature drift curve of the d-th iterationMarked as temperature drift optimization curve->And the d value is marked as the optimal scale +.>And entering a step six; when e d ≥e d-1 When the method is used, directly entering the step six;
step six, setting a threshold D, comparing the value D with the threshold D, and entering a step seven when D is more than D; otherwise, returning to the second step;
step seven, optimizing the yeast according to the temperature driftWire (C)Optimal dimension->Performing approximation stitching treatment on the infrared image X and the infrared image Y to obtain a stitched result diagram +.>
2. The infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 1, wherein the method comprises the following steps: in the first step, d=2, e 1 ＝1。
3. The infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 2, wherein the method comprises the following steps: in the second step, the first step is performed,for an m row n+2d-2 column matrix; />For an s-row n+2d-2 column matrix; wherein, expand the picture +.>The iterative formula of (2) satisfies:
wherein j=1, 2, … …, m; t=1, 2, … …, d-1;
wherein j=1, 2, …, m; t=1, 2, … …, d-1；
Wherein j=1, 2, … …, m; t=1, 2, … …, n;
expanding imagesThe iterative formula of (2) satisfies:
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, d-1;
where j=1, 2, … …, s; t=1, 2, … …, n;
wherein X is k,p Values for the kth row and the p column in the matrix X;
Y k,p values for the kth row and the p column in the matrix Y;
for matrix->The value of the kth row, p column;
for matrix->The value of the kth row, p column.
4. The infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 3, wherein the method comprises the following steps: in the third step, the multi-scale filter H is a 2d-1 dimensional vector, and specifically comprises the following steps:
H＝[12……d*(d-1)……1]/(d+d*(d-1))
the initial curve V of temperature drift is:
V＝[V 1 V 2 … V n+2d-2 ]
wherein H is k Is the kth element in the vector H;
V k is the kth element in vector V;
5. the infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 4, wherein the method comprises the following steps: in the step S42, the calculation method of the temperature drift filtering parameter matrix U is as follows:
when a=1, initializing to let U of the 1 st row and j th column of the matrix U 1,j ＝V j ，j＝1，2，……，n+2d-2；
Updating U 1,j The calculation formula of (2) is as follows:where j=d, d+1, … …, d+n-1;
in U k,p Values of the kth row and the p column in the matrix U;
H k is the kth element in vector H;
V k is the kth element in vector V;
when a is more than 1, initializing to make U a,j ＝U a-1,j ，j＝1，2，……，n+2d-2；
Updating U a,j The calculation formula of (2) is as follows:j＝d，d+1，……，d+n-1。
6. the infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 5, wherein the method comprises the following steps: in S43, the approximation graph Z satisfies:
Z 1,j ＝X m,j wherein j=1, 2, … …, n
Wherein t=1, 2, … …, s; j=1, 2, … …, n;
correction error r for the a-th iteration a The method comprises the following steps:
wherein Z is k,p Is the value of the kth row, p column in the matrix Z.
7. The infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 1, wherein the method comprises the following steps: in the sixth step, the range of the threshold D is: d is more than or equal to 12 and less than or equal to 16.
8. The infrared image stitching correction method based on temperature drift characteristic line approximation according to claim 1, wherein the method comprises the following steps: in the seventh step, the spliced result graphThe method meets the following conditions:
wherein i=1, 2, … …, m; j=1, 2, … …, n;
wherein i=1, 2, … …, s; j=1, 2, … …, n;
in the method, in the process of the invention,for matrix->The value of the kth row, p column.
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