CN108846805B - Infrared thermal image two-point non-uniform correction method based on scene self-adaption - Google Patents
Infrared thermal image two-point non-uniform correction method based on scene self-adaption Download PDFInfo
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Abstract
The invention discloses a scene self-adaptive infrared thermal image two-point non-uniform correction method, and belongs to the field of infrared thermal imaging. The method aims at the defects that the traditional two-point non-uniform correction method cannot adaptively correct offset coefficients, gain coefficients and the like along with the change of environmental temperature, so that the non-uniform correction error is larger. Firstly, solving gain coefficients and offset coefficients of two-point non-uniform correction of the infrared thermal image at different environmental temperatures; then obtaining an expression of the gain coefficient and the offset coefficient corresponding to the environmental temperature by utilizing a polynomial fitting technology; and finally, correcting the infrared thermal image in real time by using the gain coefficient and the offset coefficient which are calculated by the expression, and comparing the infrared thermal image with a traditional two-point non-uniform correction method to obtain the infrared thermal image with better correction effect and lower residual non-uniformity. The method has the advantages of simple operation, low algorithm complexity, good correction effect and the like, so the method has good application prospect and popularization value.
Description
Technical Field
The invention belongs to the field of infrared thermal imaging, and particularly belongs to non-uniform correction processing of infrared thermal images based on scene self-adaptation.
Background
Under the condition of black body uniform radiation, the output responses of all the detection units of the infrared focal plane array are completely consistent, and actually, the output responses of all the detection units have certain difference and are represented as non-uniformity. From the noise angle, the non-uniformity is caused by spatial noise mainly affected by the non-uniformity of the semiconductor material of the infrared focal plane array (doping concentration, crystal defects, internal structure non-uniformity, etc.) and the fabrication process (mask non-uniformity, lithography errors, etc.), and transient noise mainly represented by dark current noise caused by circuit design noise and ambient temperature. Transient noise can be averaged multiple times to reduce its effect, but spatial noise cannot be used in this way and non-uniform correction techniques must be used to minimize the effect of spatial noise.
On the non-uniformity correction method of the infrared thermal image, the current common correction algorithm is divided into two modes based on blackbody calibration and scene self-adaptation. The algorithm based on calibration has low complexity, and can realize real-time non-uniform correction on the existing hardware platform, so the method is widely applied to engineering. The algorithm assumes that the response of each detection unit of the infrared focal plane array is linear and time-invariant, and is calibrated by means of a uniform radiation source (black body), and finally the correction coefficient of each detection unit is obtained. The algorithm typically represents: one-point correction, two-point correction, and multi-point correction. In 1995, Schulz M and Caldwell L used a point correction algorithm for the non-uniformity of infrared thermal images, but this algorithm only corrected the non-uniformity of the focal plane array caused by doping unevenness, and did not correct the non-uniformity caused by noise such as dark current. Based on the two-point non-uniform correction algorithm and the multi-point non-uniform correction algorithm, the two-point non-uniform correction algorithm and the multi-point non-uniform correction algorithm of the infrared thermal image are provided, the non-uniformity of the infrared thermal image is reduced to a certain extent, and the quality of the infrared thermal image is improved. With the development of infrared thermal imaging technology and a large number of experimental researches, the following characteristics are shown: the response of each detection unit of the focal plane array is nonlinear time-varying, so under different environments, the non-uniformity correction coefficient is different, and multiple times of calibration are needed. In order to eliminate the tedious calibration process, Wermer Gross et al propose a scene-based adaptive non-uniformity correction algorithm, whose main idea is to continuously perform non-uniformity correction on infrared thermal images until a convergence threshold is reached. Through long-term research by experts and scholars, the algorithms are commonly found at present: algorithms such as a neural network correction method, a time domain high-pass filtering method, a kalman filtering method and the like. The algorithms have slight difference in the effect of non-uniform correction, and generally, the algorithms have the advantages of strong adaptive correction capability, little residual non-uniformity and the like, but the algorithms also have the defects of low convergence speed, high complexity and the like. These shortcomings severely limit the application of scene-based adaptive non-uniformity correction algorithms to engineering practice.
According to the analysis, the two-point non-uniformity correction method cannot adaptively correct defects such as gain coefficients and offset coefficients along with the change of the environment, so that the non-uniformity correction error is larger. The obtained neural network correction algorithm can adaptively correct the gain and the offset coefficient, but the algorithm needs a large number of image frames to be converged, so that the complexity is high, the neural network correction algorithm is difficult to use in engineering and the like. Based on the method, the two-point non-uniform correction method of the infrared thermal image based on the scene self-adaptation is provided, the method utilizes a polynomial fitting technology to adaptively correct the gain coefficient and the offset coefficient, the correction precision of the infrared thermal image is improved, the quality of the infrared thermal image is further improved, and meanwhile, the method has the advantages of simplicity in operation, low algorithm complexity and the like, so that the method has good application prospect and popularization value.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The two-point non-uniform correction method based on the scene self-adaption for the infrared thermal image is improved in quality of the infrared thermal image, simple to operate and low in algorithm complexity. The technical scheme of the invention is as follows:
a scene-adaptive infrared thermal image two-point non-uniform correction method comprises the following steps:
1) firstly, acquiring infrared thermal images of a low-temperature black body and a high-temperature black body, establishing a traditional two-point non-uniform correction model, and obtaining a gain coefficient G of each detection uniti,jAnd offset coefficient Oi,jThe expression of (1);
2) secondly, in n is notCarrying out two-point non-uniform correction at the same ambient temperature, and recording the gain coefficient G of each detection unit at the corresponding ambient temperaturei,j(Tsur) And offset coefficient Oi,j(Tsur) Then respectively plotting gain coefficients G at different ambient temperaturesi,j(Tsur) And offset coefficient Oi,j(Tsur) The scatter plot of (a); then, a quadratic polynomial fitting technology is adopted to carry out data fitting, and a gain coefficient G is obtainedi,j(Tsur) And offset coefficient Oi,j(Tsur) An expression for ambient temperature; finally according to the gain coefficient Gi,j(Tsur) And offset coefficient Oi,j(Tsur) And obtaining a scene self-adaptive infrared thermal image two-point non-uniform correction formula based on an expression between the infrared thermal image two-point non-uniform correction formula and the environment temperature, and performing real-time non-uniform correction according to the formula.
Further, the step 1) of establishing the traditional two-point non-uniformity correction model is as follows:
Yi,j＝Gi,j*Xi,j+Oi,j
in the formula, Xi,jAnd Yi,jRespectively the gray value output by the detection unit (i, j) and the corrected gray value, Gi,jAnd Oi,jRespectively gain factor and offset factor of the detection unit (i, j).
Further, the gain factor G of the detection unit (i, j)i,jAnd offset coefficient Oi,jThe calculation process of (a) is as follows: 1) adjusting the temperature of the black body to a low temperature TLAnd adjusting the position to make the black body radiation plane completely cover the infrared focal plane array, when the temperature of the black body is stabilized at TLCollecting black body and storing grey value X of each pixel of infrared thermal imagei,j(TL) And calculating the average gray value at the temperature
As shown in the following formula
In the formula, M and N respectively represent the total row number and the column number of the infrared thermal image, and i and j respectively represent the row and the column of the pixel.
2) Adjusting the temperature of the black body to a high temperature THAnd adjusting the position to make the black body radiation surface completely cover the infrared focal plane array, when the temperature of the black body is stabilized at THCollecting black body and storing gray value X of each pixel of infrared thermal imagei,j(TH) And calculating the average gray value at the temperature
As shown in the following formula:
3) calculating the gain coefficient Gi,jAnd offset coefficient Oi,j
In the formula
Indicating a high temperature T of the black bodyHThe average gray scale value of the time-infrared thermal image,
indicating a black body temperature of low temperature TLMean gray value of time-infrared thermal image, Xi,j(TH) Indicating a high temperature T of the black bodyHThe position of the image element in the time-infrared thermal image is a gray value of (i, j), Xi,j(TL) Indicating a black body temperature of low temperature TLThe position of the image element in the time-infrared thermal image is a gray value of (i, j), TLAnd THRespectively representing low temperature of black bodyTemperature and high temperature, TLAnd THMust be selected in the linear region of the response curve.
Further, in the step 2), a quadratic polynomial is adopted for fitting to obtain a gain coefficient and an offset coefficient under different environmental temperatures, and finally, a scene-adaptive infrared thermal image two-point non-uniform correction formula is obtained, wherein the process is as follows:
1) carrying out two-point non-uniform correction at n different environmental temperatures, and recording the gain coefficient G of each detection unit at the corresponding environmental temperaturei,j(Tsur) And offset coefficient Oi,j(Tsur) Here, TsurRepresents ambient temperature;
2) and drawing the gain coefficient G under different environmental temperaturesi,j(Tsur) Fitting the data by using a quadratic polynomial fitting technology to obtain the following expression:
Gi,j(Tsur)＝Ai,jTsur 2+Bi,jTsur+Ci,j
3) and drawing the offset coefficient O under different environmental temperaturesi,j(Tsur) Fitting the data by using a quadratic polynomial fitting technology to obtain the following expression:
Oi,j(Tsur)＝Di,jTsur 2+Ei,jTsur+Fi,j
4) and obtaining a gain coefficient and an offset coefficient under the corresponding temperature according to the 2) and the 3), and obtaining a scene-based self-adaptive non-uniformity correction formula as follows:
Yi,j(T)＝(Ai,jTsur 2+Bi,jTsur+Ci,j)Xi,j+(Di,jTsur 2+Ei,jTsur+Fi,j)
in the formula Ai,j，Bi,j，Ci,j，Di,j，Ei,j，Fi,jIs the correction factor of the detection unit (i, j).
Further, the environment temperature for performing the two-point non-uniformity correction is 6-40 ℃, wherein the interval of the environment temperature is 2 ℃.
The invention has the following advantages and beneficial effects:
the innovation point of the method is that step 2), firstly, two-point non-uniform correction is carried out at different environmental temperatures, a gain coefficient and an offset coefficient under the corresponding environmental temperatures are obtained, then, an expression between the gain coefficient and the offset coefficient and the environmental temperatures is obtained by adopting a polynomial fitting technology, and finally, the obtained expression is utilized to carry out the non-uniform correction of the infrared thermal image. Therefore, the method reduces the error of the traditional two-point non-uniform correction method adopting the fixed gain coefficient and the offset coefficient, improves the quality of the infrared thermal image, has the advantages of simple operation, low algorithm complexity and the like, and can be applied and popularized in engineering.
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FIG. 1 is a schematic diagram of the present invention providing two exemplary non-uniformity corrections;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a schematic diagram showing a comparison of blackbody effects of non-uniformity correction algorithms;
FIG. 4 is a schematic diagram showing the comparison of the real-world effects of the non-uniformity correction algorithms.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
first, a two-point non-uniformity correction model is built (as shown in FIG. 1). Then, gain coefficients G of all detection units under different environmental temperatures are calculated according to the modeli,j(Tsur) And offset coefficient Oi,j(Tsur) And calculating the gain coefficient G by a quadratic polynomial fitting techniquei,j(Tsur) And offset coefficient Oi,j(Tsur) And ambient temperature. Finally obtainTo a two-point non-uniformity correction formula for infrared thermal images based on scene adaptation (as shown in fig. 2). The implementation of the various steps is described in detail below
1. The traditional two-point non-uniformity correction model is established as follows:
Yi,j＝Gi,j*Xi,j+Oi,j
in the formula, Xi,jAnd Yi,jRespectively the gray value output by the detection unit (i, j) and the corrected gray value, Gi,jAnd Oi,jRespectively gain factor and offset factor of the detection unit (i, j). It Gi,jAnd Oi,jThe calculation process is as follows:
1) adjusting the temperature of the black body to a low temperature TLAnd adjusting the position to make the black body radiation surface completely cover the infrared focal plane array, when the temperature of the black body is stabilized at TLCollecting black body and storing gray value X of each pixel of infrared thermal imagei,j(TL) And calculating the average gray value at the temperature
As shown in the following formula
In the formula, M and N respectively represent the total row number and the column number of the infrared thermal image, and i and j respectively represent the row and the column of the pixel.
2) Adjusting the temperature of the black body to a high temperature THAnd adjusting the position to make the black body radiation surface completely cover the infrared focal plane array, when the temperature of the black body is stabilized at THCollecting black body and storing gray value X of each pixel of infrared thermal imagei,j(TH) And calculating the average gray value at the temperature
As shown in the following formula:
3) calculating the gain coefficient Gi,jAnd offset coefficient Oi,j
Note that: t isLAnd THMust be selected in the linear region of the response curve, while THAnd TLThe difference of (a) is as large as possible, so that the correction range is wide.
2. Solving gain coefficient G under different environmental temperatures according to two-point non-uniform correction modeli,j(Tsur) And offset coefficient Oi,j(Tsur) And calculating the gain coefficient G by a quadratic polynomial fitting techniquei,j(Tsur) And offset coefficient Oi,j(Tsur) And finally obtaining a scene self-adaptive infrared thermal image two-point non-uniform correction formula based on an expression between the infrared thermal image two-point non-uniform correction formula and the environment temperature. The process is as follows:
1) carrying out two-point non-uniform correction at the ambient temperature of 6-40 ℃, and recording the gain coefficient G of each detection unit at the corresponding ambient temperaturei,j(Tsur) And offset coefficient Oi,j(Tsur) Wherein the ambient temperature interval is 2 ℃, T heresurRepresenting the ambient temperature.
2) And drawing the gain coefficient G under different environmental temperaturesi,j(Tsur) Fitting the data by using a quadratic polynomial fitting technology to obtain the following expression:
Gi,j(Tsur)＝Ai,jTsur 2+Bi,jTsur+Ci,j
3) and drawing the offset coefficient O under different environmental temperaturesi,j(Tsur) The scatter diagram is obtained by adopting a quadratic polynomial fitting technologyFitting of the line data yields the following expression:
Oi,j(Tsur)＝Di,jTsur 2+Ei,jTsur+Fi,j
4) and obtaining a gain coefficient and an offset coefficient under the corresponding temperature according to the 2) and the 3), and obtaining a scene-based self-adaptive non-uniform correction formula as follows:
Yi,j(T)＝(Ai,jTsur 2+Bi,jTsur+Ci,j)Xi,j+(Di,jTsur 2+Ei,jTsur+Fi,j)
in the formula Ai,j，Bi,j，Ci,j，Di,j，Ei,j，Fi,jIs the correction factor of the detection unit (i, j).
3. Simulation verification through infrared thermal image
And selecting an infrared thermal image with the resolution of 640 multiplied by 512 for simulation verification, and performing real-time non-uniform correction by using a traditional two-point non-uniform correction algorithm, a neural network non-uniform correction algorithm and the algorithm provided by the invention. And comparing the performances of the three correction algorithms from multiple dimensions such as real-time correction speed, blackbody uniform radiation, residual non-uniformity and specific scenes.
1) Comparison of real-time corrected speeds
The speed of real-time non-uniform correction is an important index for evaluating a correction algorithm, the TMS320DM6437 video processing front end used by the invention is configured to be 54MHZ, and under the same experimental environment, three different non-uniform correction algorithms are compared, and the comparison result is shown in Table 1
TABLE 1 speed contrast table for different correction algorithms
As can be seen from Table 1, the algorithm provided by the invention is superior to the neural network non-uniform correction algorithm in real time, and has a small difference in display frame rate compared with the two-point non-uniform correction algorithm. The two-point non-uniform correction needs multiplication and addition operation for each data once, meanwhile, the algorithm provided by the invention needs multiplication and addition operation for each data for many times, and the neural network non-uniform correction needs more than 1000 frames of infrared thermal images to enable the algorithm to be converged, so that the algorithm complexity is high.
2) Comparison of correction algorithms based on blackbody uniform radiation
Blackbody infrared thermal images at 70 ℃ were first acquired at ambient temperatures of 10 ℃ and 35 ℃ respectively, as shown in fig. 3(a) and 3 (b). The two acquired images are then corrected using two-point non-uniformity, as shown in fig. 3(c) and 3 (d). The acquired infrared thermal images were then corrected for non-uniformity using a neural network, as shown in fig. 3(e) and 3 (f). Finally, the acquired infrared thermal image is subjected to non-uniform correction by using the algorithm proposed by the invention, as shown in fig. 3(m) and 3 (n).
As can be seen from fig. 3, there are significant vertical stripes in both the graph (a) and the graph (b), which are caused by the non-uniform response of the detection units in the infrared focal plane and appear as non-uniformity in the image. Note also that the luminance of graph (b) is higher than graph (a) and the vertical stripes have some difference due to the shift of the gray value caused by the change of the ambient temperature. The graphs (c) and (d) are infrared thermal images corrected using two-point non-uniformity, whose vertical streaks are significantly reduced, but since the gain coefficient and the offset coefficient are fixed values, the non-uniformity due to temperature drift cannot be effectively corrected, so that the residual non-uniformity of the graphs (c) and (d) is arbitrarily large. The graphs (e) and (f) are infrared thermal images corrected for non-uniformity using a neural network, which has few vertical stripes and few non-uniform points, because the neural network can adapt to environmental changes by modifying the gain coefficient and the offset coefficient through continuous iteration, but the method is more complex and is not suitable for systems with higher real-time requirements. The images (m) and (n) are infrared thermal images corrected by the method provided by the invention, vertical stripes hardly exist, and residual non-uniformity of the two images is small and approximately consistent, because the gain coefficient and the offset coefficient are subjected to quadratic polynomial fitting according to different environmental temperatures, the method can adapt to environmental changes, well solves the problem of non-uniformity of the infrared thermal images caused by the environmental temperatures, and meanwhile, the algorithm has small complexity and can be applied to an infrared thermal imaging system with high real-time performance.
3) Comparison of residual non-uniformity
The residual non-uniformity of each infrared thermal image can be derived from fig. 3, as shown in table 2.
TABLE 2 residual inhomogeneities of IR thermographic images by different correction algorithms
As can be seen from the above table, the two points of infrared thermal images after non-uniformity correction have better improved residual non-uniformity, but the correction results are not consistent under different environmental temperatures. The infrared thermal image corrected by the algorithm provided by the invention has small residual nonuniformity and is relatively stable under different environmental temperatures, because the gain coefficient and the offset coefficient can be adaptive to the change of the environment. Although the neural network non-uniformity correction algorithm has the advantages of small residual non-uniformity, good stability and the like, the algorithm is high in complexity and cannot be applied to an infrared thermal imaging system with high real-time requirement.
4) Comparison of correction algorithms based on live-action
The advantages and the disadvantages of all correction algorithms are compared through the infrared thermal images uniformly radiated by the black body, and the residual non-uniformity of the corrected infrared thermal images is combined to prove that the algorithm provided by the invention has the advantages of good correction effect, self-adaption to environmental change and the like. Next, the advantages and disadvantages of the correction algorithms will be compared more intuitively for the specific real scene, as shown in fig. 4, which respectively shows the original infrared thermal image at the ambient temperature of 10 ℃ and 35 ℃, the conventional two-point corrected infrared thermal image, and the corrected infrared thermal image of the present invention.
As can be seen from fig. 4, the residual non-uniformity is larger and has a certain difference between fig. 4(c) and fig. 4(d), for example, the residual non-uniformity on the right side of fig. 4(d) is larger than the residual non-uniformity on the left side of fig. 4 (c). The residual non-uniformity is less and the distribution is approximately similar in fig. 4(e) and fig. 4 (f). The two-point non-uniformity correction adopts fixed gain coefficients and offset coefficients, so that the non-uniformity caused by temperature offset cannot be corrected, but the gain coefficients and the offset coefficients of the algorithm are generated by fitting the ambient temperature and can be adaptive to the environmental change.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (4)
1. A scene self-adaptive infrared thermal image two-point non-uniform correction method is characterized by comprising the following steps:
1) firstly, acquiring infrared thermal images of a low-temperature black body and a high-temperature black body, establishing a traditional two-point non-uniformity correction model, and obtaining a gain coefficient G of each detection uniti,jAnd offset coefficient Oi,jThe expression of (1);
2) secondly, carrying out two-point non-uniform correction under n different environmental temperatures, and recording the gain coefficient G of each detection unit under the corresponding environmental temperaturei,j(Tsur) And offset coefficient Oi,j(Tsur)，TsurRepresenting the ambient temperature, and then respectively drawing the gain coefficients G at different ambient temperaturesi,j(Tsur) And offset coefficient Oi,j(Tsur) The scatter plot of (a); then, a quadratic polynomial fitting technology is adopted to carry out data fitting, and a gain coefficient G is obtainedi,j(Tsur) And offset coefficient Oi,j(Tsur) An expression for ambient temperature; finally according to the gain coefficient Gi,j(Tsur) And offset coefficient Oi,j(Tsur) Obtaining a scene self-adaptive infrared thermal image two-point non-uniform correction formula based on an expression between the infrared thermal image two-point non-uniform correction formula and the environment temperature, and performing real-time non-uniform correction according to the formula;
in the step 2), a quadratic polynomial is adopted for fitting to obtain a gain coefficient and an offset coefficient under different environmental temperatures, and finally, a scene-adaptive infrared thermal image two-point non-uniform correction formula is obtained, wherein the process is as follows:
1) carrying out two-point non-uniform correction at n different environmental temperatures, and recording the gain coefficient G of each detection unit at the corresponding environmental temperaturei,j(Tsur) And offset coefficient Oi,j(Tsur) Here, TsurRepresents ambient temperature;
2) and drawing the gain coefficient G under different environmental temperaturesi,j(Tsur) Fitting data by using a quadratic polynomial fitting technology to obtain the following expression:
Gi,j(Tsur)＝Ai,jTsur 2+Bi,jTsur+Ci,j
3) and drawing the offset coefficient O under different environmental temperaturesi,j(Tsur) Fitting data by using a quadratic polynomial fitting technology to obtain the following expression:
Oi,j(Tsur)＝Di,jTsur 2+Ei,jTsur+Fi,j
4) and obtaining a gain coefficient and an offset coefficient under the corresponding temperature according to the 2) and the 3), and obtaining a scene-based self-adaptive non-uniformity correction formula as follows:
Yi,j(T)＝(Ai,jTsur 2+Bi,jTsur+Ci,j)Xi,j+(Di,jTsur 2+Ei,jTsur+Fi,j)
in the formula Ai,j，Bi,j，Ci,j，Di,j，Ei,j，Fi,jIs the correction factor of the detection unit (i, j).
2. The scene-adaptive two-point non-uniformity correction method for the infrared thermal image based on the scene of claim 1, wherein the step 1) of establishing a traditional two-point non-uniformity correction model comprises the following steps:
Yi,j＝Gi,j*Xi,j+Oi,j
in the formula, Xi,jAnd Yi,jRespectively the gray value output by the detection unit (i, j) and the corrected gray value, Gi,jAnd Oi,jRespectively gain factor and offset factor of the detection unit (i, j).
3. The scene-adaptive two-point non-uniformity correction method for infrared thermal images according to claim 2, wherein the gain factor G of the detection unit (i, j)i,jAnd offset coefficient Oi,jThe calculation process of (a) is as follows: 1) adjusting the temperature of the black body to a low temperature TLAnd adjusting the position to make the black body radiation surface completely cover the infrared focal plane array, when the temperature of the black body is stabilized at TLCollecting black body and storing gray value X of each pixel of infrared thermal imagei,j(TL) And calculating the average gray value at the temperature
As shown in the following formula
In the formula, M and N respectively represent the total row number and the column number of the infrared thermal image, and i and j respectively represent the row and the column where the pixel is located;
2) adjusting the temperature of the black body to a high temperature THAnd adjusting the position to make the black body radiation surface completely cover the infrared focal plane array, when the temperature of the black body is stabilized at THCollecting black body and storing gray value X of each pixel of infrared thermal imagei,j(TH) And calculate thisMean gray value at temperature
As shown in the following formula:
3) calculating the gain coefficient Gi,jAnd offset coefficient Oi,j
In the formula
Indicating a high temperature T of the black bodyHThe average gray scale value of the time-infrared thermal image,
indicating a black body temperature of low temperature TLMean gray value of time-infrared thermal image, Xi,j(TH) Indicating a high temperature T of the black bodyHThe position of the image element in the time-infrared thermal image is a gray value of (i, j), Xi,j(TL) Indicating a black body temperature of low temperature TLThe position of the image element in the time-infrared thermal image is a gray value of (i, j), TLAnd THRespectively, the low temperature and the high temperature of the black body, and TLAnd THMust be selected in the linear region of the response curve.
4. The scene-adaptive two-point non-uniform correction method for the infrared thermal image based on the scene adaptation of claim 1, wherein the two-point non-uniform correction is performed at an ambient temperature of 6 ℃ to 40 ℃ and at an interval of 2 ℃.
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| CN109655162A (en) * | 2018-11-30 | 2019-04-19 | 诺仪器（中国）有限公司 | Thermal infrared imager temperature measurement correction system and method |
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