A fine calibration method and device for an infrared thermal imager

Abstract

The application discloses a fine calibration method and device for an infrared thermal imager, and the method comprises the following steps: acquiring temperature value range information of a blackbody radiation source; setting the temperature of the blackbody radiation source based on the temperature value range information, measuring the temperature of the blackbody radiation source by using the infrared thermal imager, and obtaining a measured temperature set; and processing the measured temperature set to obtain calibration information of the infrared thermal imager.

Description

Technical Field

[0001] This invention relates to the fields of industrial data mining and processing, infrared image measurement, and strategy optimization, specifically to a refined calibration method and apparatus for infrared thermal imagers. Background Technology

[0002] An infrared thermal imager is a device that uses an infrared optical system, an infrared detector, and an electronic processing system to convert the infrared thermal radiation emitted from an object's surface into a visible light image and display it on a screen. In recent years, infrared thermal imagers have been widely used in security monitoring and medical surveillance. Therefore, the correction of temperature measurement errors in infrared thermal imagers is of great significance to their development.

[0003] Infrared radiation thermometry employs a non-contact measurement method, featuring fast response, long measurement distance, wide temperature range, and no interference with the target being measured. However, the use of thermal imagers, the aging of electronic components, and changes in the working environment can all introduce certain measurement errors. These errors mainly include the following two aspects: (1) Nonlinear error within the measurement range The raw signal measured by each pixel of a thermal imager is usually called the OS (Original Signal), which is the fundamental quantity for data transmission, processing, and display within the thermal imager. OS represents the amplitude of the output level of the infrared sensor. Influenced by the photoelectric characteristics of the material readout, the characteristics of the circuitry, and the filtering circuitry, the relationship between OS and the equivalent blackbody radiation temperature is not linear. With prolonged use, thermal imagers have shown a gradual deviation from their factory calibration curves, leading to increased temperature measurement errors.

[0004] (2) Inconsistency error in pixel temperature measurement Thermal imager lenses are typically circular, while the imaging plane is rectangular. Due to the lens size, thermal imagers can also exhibit a "vignetting effect" similar to that of cameras, where insufficient light enters the four corners of the rectangular image, resulting in a significant difference between the corner areas and the central area. Furthermore, because the photosensitive components and readout circuits of each pixel are independent, even slight differences in materials and components can cause "noise" in the thermal imager, and individual pixel malfunctions can even result in "dead pixels," leading to inconsistent temperature measurements from the m×n pixels that make up the thermal image. Summary of the Invention

[0005] This invention primarily addresses the problem of how to accurately and rapidly calibrate infrared thermal imagers. It discloses a refined calibration method and apparatus for infrared thermal imagers.

[0006] In a first aspect, this invention discloses a method for fine calibration of an infrared thermal imager, comprising: S1, Obtain the temperature range information of the blackbody radiation source; the temperature range information includes several blackbody temperature values; S2, based on the temperature range information, set the temperature of the blackbody radiation source, and use an infrared thermal imager to measure the temperature of the blackbody radiation source to obtain a set of measured temperatures; S3, process the measured temperature set to obtain the calibration information of the infrared thermal imager.

[0007] Based on the temperature range information, the temperature of the blackbody radiation source is set, and the temperature of the blackbody radiation source is measured using an infrared thermal imager to obtain a set of measured temperatures, including: S21, Set a blackbody radiation source, and place it under each blackbody temperature value in the temperature range information in sequence; S22, at each blackbody temperature value, ensures that the blackbody radiation source fills the field of view of the infrared thermal imager; S23, at each blackbody temperature value, the temperature of the blackbody radiation source is measured using an infrared thermal imager to obtain the temperature values ​​of all pixels of the infrared thermal imager; using the temperature values ​​of all pixels, a subset of measured temperatures corresponding to the blackbody temperature value is constructed. S24, using the temperature values ​​of all pixels of the infrared thermal imager obtained under all blackbody temperature values ​​of the blackbody radiation source, a set of measurement temperatures is constructed; the set of measurement temperatures includes a subset of measurement temperatures corresponding to each blackbody temperature value.

[0008] The process of processing the measured temperature set to obtain the calibration information of the infrared thermal imager includes: S31, for each subset of the measured temperatures in the set of measured temperatures, subtract the corresponding blackbody temperature value to obtain the corresponding subset of the difference temperatures; S32, preprocess all the differential temperature subsets to obtain the differential temperature set; S33, perform correction and compensation processing on the differential temperature set and temperature value range information to obtain the temperature compensation value for all blackbody temperature values; S34, confirm the temperature compensation values ​​for all blackbody temperature values, which are the calibration information for the infrared thermal imager.

[0009] The preprocessing of all differential temperature subsets yields a differential temperature set, including: S321, Perform outlier removal on all differential temperature subsets to obtain the first differential temperature subset corresponding to each differential temperature subset; S322, filter each first differential temperature subset to obtain a differential temperature set.

[0010] The step of correcting and compensating the differential temperature set and temperature range information yields temperature compensation values ​​for all blackbody temperature values, including: S331, For each subset of the difference temperature set, perform joint difference region extraction to obtain the joint difference region; S332, calculate the difference feature value of the joint difference region to obtain the difference feature value of the difference temperature subset; S333, determine the difference characteristic value of the difference temperature subset, which is the temperature compensation value under the blackbody temperature value corresponding to the difference temperature subset; S334, for all subsets of differential temperatures, execute S331 to S333 to obtain the temperature compensation values ​​for all blackbody temperature values.

[0011] The step of extracting a joint difference region for each subset of the difference temperature set to obtain a joint difference region includes: The first image information corresponding to each subset of temperature differences is uniformly divided to obtain several sub-images; Set the number of pixels n1 in the row direction and the number of pixels m1 in the column direction of the sub-image; Based on the first image information of each temperature difference subset, a difference region extraction model is constructed from all sub-images. The difference region extraction model is solved to obtain the joint difference region.

[0012] The expression for the difference region extraction model is: , In the formula, Represents the correlation quantity Parameters for taking the maximum value The value of , To determine the starting point coordinates of the first sub-image of the obtained joint difference region, The coordinates of the starting point of the first sub-image in the model are assigned. and These represent the pixel values ​​at coordinates [k+i, l+j] and [k+i+n1, l+j+m1] in the first image information, respectively. This represents the mean value of all pixels in the sub-image with starting point coordinates [k, l]. This represents the mean value of all pixels in the sub-image with starting point coordinates [k+n1, l+m1], where n and m are the row and column dimensions of the sub-image, respectively. This represents the average value of all pixels in the first image.

[0013] A second aspect of this invention discloses a fine calibration device for an infrared thermal imager, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the fine calibration method of the infrared thermal imager.

[0014] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the fine calibration method for the infrared thermal imager.

[0015] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the fine calibration method for the infrared thermal imager.

[0016] The beneficial effects of this invention are as follows: This invention corrects temperature measurement errors in thermal imagers by using a large-area blackbody radiation source. It employs a difference algorithm to correct nonlinearity errors and measurement consistency errors between pixels within a given interval, thereby improving the accuracy of infrared thermal imaging temperature measurement. This invention uses the standard deviation of the equivalent blackbody radiation temperature measured by all pixels of the thermal imager as the basis for evaluating the temperature measurement consistency of the infrared thermal imager. It establishes a standard for judging temperature measurement consistency and assesses whether the infrared thermal imager meets the required specifications by evaluating the standard deviation of the equivalent blackbody radiation temperature of mid-wave and long-wave thermal imagers.

[0017] Based on a preset temperature range, this invention places the blackbody radiation source at various temperatures and fills the field of view of the infrared thermal imager, collecting measurement data from all pixels. This ensures that the temperature information of each pixel under different temperature conditions is completely captured, providing comprehensive and complete basic data for subsequent calibration and avoiding calibration deviations caused by missing local data.

[0018] This invention calculates the difference between the measurement data at each temperature and the corresponding blackbody standard temperature, directly locking the temperature measurement error of each pixel. This makes the error information more intuitive and quantifiable, providing a clear analytical object for subsequent error processing and compensation value calculation. It avoids the problem of errors being masked in the original measurement data and improves the pertinence of calibration analysis.

[0019] The calibration information output by this invention includes temperature compensation values ​​at all blackbody temperatures. Each compensation value corresponds to the error characteristics of a specific temperature and a specific pixel area, which can provide a refined calibration basis for infrared thermal imagers. This enables the calibration operation to specifically correct errors caused by inconsistent pixel temperature measurement and nonlinear range, significantly improving the temperature measurement accuracy of infrared thermal imagers across the entire temperature range and pixel area, and broadening their application boundaries in high-precision temperature measurement scenarios. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0021] To better understand the content of this invention, an embodiment is provided here.

[0022] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0023] In a first aspect, this invention discloses a method for fine calibration of an infrared thermal imager, comprising: S1, obtain the temperature range information of the blackbody radiation source; S2, based on the temperature range information, set the temperature of the blackbody radiation source, and use an infrared thermal imager to measure the temperature of the blackbody radiation source to obtain a set of measured temperatures; S3, process the measured temperature set to obtain the calibration information of the infrared thermal imager.

[0024] Based on the temperature range information, the temperature of the blackbody radiation source is set, and the temperature of the blackbody radiation source is measured using an infrared thermal imager to obtain a set of measured temperatures, including: The temperature range information includes several blackbody temperature values; S21, Set a blackbody radiation source, and place it under each blackbody temperature value in the temperature range information in sequence; S22, at each blackbody temperature value, ensures that the blackbody radiation source fills the field of view of the infrared thermal imager; S23, at each blackbody temperature value, the temperature of the blackbody radiation source is measured using an infrared thermal imager to obtain the temperature values ​​of all pixels of the infrared thermal imager; using the temperature values ​​of all pixels, a subset of measured temperatures corresponding to the blackbody temperature value is constructed.

[0025] S24, using the temperature values ​​of all pixels of the infrared thermal imager obtained under all blackbody temperature values ​​of the blackbody radiation source, a set of measurement temperatures is constructed; the set of measurement temperatures includes a subset of measurement temperatures corresponding to each blackbody temperature value.

[0026] The process of processing the measured temperature set to obtain the calibration information of the infrared thermal imager includes: S31, for each subset of the measured temperatures in the set of measured temperatures, subtract the corresponding blackbody temperature value to obtain the corresponding subset of the difference temperatures; S32, preprocess all the differential temperature subsets to obtain the differential temperature set; S33, perform correction and compensation processing on the differential temperature set and temperature value range information to obtain the temperature compensation value for all blackbody temperature values; S34, confirm the temperature compensation values ​​for all blackbody temperature values, which are the calibration information for the infrared thermal imager.

[0027] Subtracting the measured temperature subset from the corresponding blackbody temperature value involves subtracting each element of the measured temperature subset from its corresponding blackbody temperature value to obtain the corresponding difference temperature subset. The preprocessing of all differential temperature subsets yields a differential temperature set, including: S321, Perform outlier removal on all differential temperature subsets to obtain the first differential temperature subset corresponding to each differential temperature subset; S322, filter each first differential temperature subset to obtain a differential temperature set.

[0028] The filtering process for each first subset of temperature differences to obtain a set of temperature differences includes: Each first subset of temperature differences is represented as a corresponding temperature difference image; the gray value of each pixel in the temperature difference image is the temperature difference of the corresponding pixel in the first subset of temperature differences. The coordinates of the pixels in the temperature difference image are determined by the coordinates of the corresponding pixels in the first temperature difference subset.

[0029] For each temperature difference image, Gaussian filtering and mean filtering are performed respectively to obtain the corresponding first filtered image information and second filtered image information; The first filtered image information and the second filtered image information are weighted and summed to obtain the first image information. The grayscale values ​​of all pixels in each first image are used as the temperature difference values ​​of the corresponding differential temperature subset in the differential temperature set.

[0030] The weighted summation process involves performing a weighted summation on each pixel of the first filtered image information and the second filtered image information to obtain the corresponding pixel in the first image information. The weight values ​​used can be 0.6 and 0.4.

[0031] The outlier can be identified using a Kalman filter. The imputation value for missing values ​​can be determined by averaging the measurements within a certain sampling interval before and after the missing value.

[0032] This invention first removes outliers from the differential temperature data, and then performs filtering by weighted summation of Gaussian filtering and mean filtering. Outlier removal can effectively remove abnormal interference data. The combination of dual filtering not only preserves the core characteristics of differential temperature, but also smooths out random noise, avoiding feature loss or noise residue caused by single filtering, and significantly improving the reliability and stability of differential temperature data.

[0033] This invention constructs a difference region extraction model and performs correlation analysis on the divided sub-images to accurately locate joint difference regions with consistent difference characteristics. These regions can centrally reflect the systematic temperature measurement errors of the infrared thermal imager, avoiding the inefficiency and lack of specificity caused by indiscriminate analysis of the overall image. This allows error analysis to focus more on the core region and improves the efficiency of the calibration process.

[0034] The step of correcting and compensating the differential temperature set and temperature range information yields temperature compensation values ​​for all blackbody temperature values, including: S331, For each subset of the difference temperature set, perform joint difference region extraction to obtain the joint difference region; S332, calculate the difference feature value of the joint difference region to obtain the difference feature value of the difference temperature subset; S333, determine the difference characteristic value of the difference temperature subset, which is the temperature compensation value under the blackbody temperature value corresponding to the difference temperature subset; S334, for all subsets of differential temperatures, execute S331 to S333 to obtain the temperature compensation values ​​for all blackbody temperature values.

[0035] The step of extracting a joint difference region for each subset of the difference temperature set to obtain a joint difference region includes: The first image information corresponding to each subset of temperature differences is uniformly divided to obtain several sub-images; Set the number of pixels n1 in the row direction and the number of pixels m1 in the column direction of the sub-image; Based on the first image information of each temperature difference subset, a difference region extraction model is constructed from all sub-images. The expression for the difference region extraction model is: , In the formula, Represents the correlation quantity Parameters for taking the maximum value The value of , To determine the starting point coordinates of the first sub-image of the obtained joint difference region, The coordinates of the starting point of the first sub-image in the model are assigned. and These represent the pixel values ​​at coordinates [k+i, l+j] and [k+i+n1, l+j+m1] in the first image information, respectively. This represents the mean value of all pixels in the sub-image with starting point coordinates [k, l]. This represents the mean value of all pixels in the sub-image with starting point coordinates [k+n1, l+m1], where n and m are the row and column dimensions of the sub-image, respectively. This represents the average value of all pixels in the first image.

[0036] The difference region extraction model is solved to obtain the joint difference region.

[0037] Solving the difference region extraction model yields a joint difference region, including: Solving the difference region extraction model yields... ,according to The first sub-image of the joint difference region is determined, and the sub-images of the first sub-image with row-direction spacing of pixels n1 and column-direction spacing of pixels m1 are determined as the second sub-image. Using the first and second sub-images, a joint difference region of the difference temperature subset is constructed.

[0038] The formula for the difference region extraction model quantifies the correlation between different sub-images and determines the location of key sub-images by targeting the maximum correlation. This accurately filters out combinations of sub-images with consistent difference characteristics, forming joint difference regions. Its core advantage lies in quantifying the degree of correlation between sub-images through mathematical operations, avoiding subjective errors caused by manual screening or single threshold judgments. This ensures that the extracted joint difference regions accurately reflect the systematic error distribution of the infrared thermal imager, rather than random errors or local noise. Simultaneously, the formula clearly defines the range constraints and interval requirements of the sub-images, guaranteeing the standardization and effectiveness of region extraction. This avoids misjudgments caused by sub-images exceeding image boundaries or unreasonable intervals, providing accurate and reliable region positioning basis for subsequent error feature analysis. This allows error analysis to focus on the core areas that truly affect temperature measurement accuracy.

[0039] The solution to the difference region extraction model can be achieved using an exhaustive method. The parameters... The values ​​of should ensure that the pixel coordinates of each sub-image do not exceed the range of pixel values ​​in the first image information.

[0040] The directions of the number of pixels n1 in the row direction and the number of pixels m1 in the column direction are rightward in the row direction and downward in the column direction, respectively.

[0041] The step of calculating the difference feature values ​​of the joint difference region to obtain the difference feature values ​​of the difference temperature subset includes: For the joint difference region of each difference temperature subset, the difference characteristics are calculated separately to obtain the difference value and weight value; The difference values ​​and weight values ​​of the difference temperature subset are weighted and fused to obtain the difference feature values ​​of the difference temperature subset.

[0042] The calculation of the difference features includes: The first and second sub-images of the joint difference region are represented by a first matrix and a second matrix, respectively; the elements in the first and second matrices are the pixel values ​​in the first and second sub-images, respectively, and the coordinates of the elements in the first and second matrices are the position coordinates of the pixel points in the first and second sub-images, respectively. Add the first matrix and the second matrix to obtain the cumulative matrix; The maximum gradient values ​​in the horizontal and vertical directions of the accumulated matrix are calculated respectively, and the feature matrix is ​​constructed based on all the maximum gradient values. The expression for the feature matrix is: in, and These are the maximum gradient values ​​in the horizontal and vertical directions, respectively; Perform a matrix trace operation on the feature matrix to obtain the matrix trace; determine the matrix trace as the difference value; The eigenvalues ​​of the accumulated matrix are calculated to obtain the largest eigenvalue; the largest eigenvalue is then determined as the weight value. The expression of the feature matrix integrates the maximum gradient information of the joint difference region in both the horizontal and vertical axes, systematically correlating and representing the gradient features in the two directions through matrix form. Gradient information can intuitively reflect the drastic degree of temperature change within the difference region and is a key feature reflecting the error intensity. The matrix representation allows this two-dimensional change feature to be structured and quantified, avoiding the one-sidedness of single-direction gradient analysis. By performing trace value calculation on this matrix, the two-dimensional gradient features can be transformed into a single, quantifiable difference index. This index can comprehensively and objectively reflect the overall error intensity of the difference region, retaining the core information of the gradient features while simplifying and focusing the features. This provides scientific mathematical support for the subsequent accurate calculation of difference values, ensuring that the difference values ​​can truly reflect the actual situation of the error.

[0043] The expression for the weight fusion calculation is: in, and Let be the weight value and the difference value of the i-th differential temperature subset, respectively. The maximum value of the weights for all subsets of temperature differences. Let be the difference characteristic value of the i-th differential temperature subset.

[0044] The weighted fusion calculation introduces the maximum weight value under all temperature conditions as a reference benchmark, and fuses the weight value and difference value corresponding to each temperature to form the final difference feature value. Its core advantage lies in constructing a fusion logic that considers both importance and intensity: the weight value reflects the importance of the corresponding difference region, while the difference value reflects the actual intensity of the error. The fusion of these two avoids underestimation of errors in key areas due to a sole reliance on the difference value, or misjudgment of error intensity due to a sole reliance on the weight value. Simultaneously, through the benchmarking of the maximum weight value, the difference feature values ​​at different temperatures have a unified comparable standard, clearly distinguishing the error contribution of different temperature ranges, highlighting the influence of key temperature ranges and key difference regions, and avoiding isolated analysis of error characteristics at each temperature. This fusion method allows the final temperature compensation value to accurately match the error patterns under different temperature conditions, ensuring that the calibration information can adapt to the temperature measurement needs of the entire temperature range, and improving the adaptability and accuracy of the calibration results.

[0045] This invention extracts gradient features and performs matrix operations on joint difference regions, transforming two-dimensional regional difference features into quantified difference values. At the same time, it determines weight values ​​through feature value calculation, thereby achieving the fusion of difference values ​​and weight values. This approach comprehensively considers the intensity of temperature changes in difference regions and highlights the importance of key difference regions, allowing the calculated difference feature values ​​to truly and accurately reflect the error patterns at different temperatures.

[0046] In all embodiments of the present invention, the variables involved in all computational expressions or mathematical functions have been dimensionlessized before computation.

[0047] In all embodiments of the present invention, the values ​​of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.

[0048] A second aspect of this invention discloses a fine calibration device for an infrared thermal imager, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the fine calibration method of the infrared thermal imager.

[0049] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the fine calibration method for the infrared thermal imager.

[0050] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the fine calibration method for the infrared thermal imager.

[0051] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A refined calibration method for an infrared thermal imager, characterized in that, include: S1, Obtain the temperature range information of the blackbody radiation source; the temperature range information includes several blackbody temperature values; S2, based on the temperature range information, set the temperature of the blackbody radiation source, and use an infrared thermal imager to measure the temperature of the blackbody radiation source to obtain a set of measured temperatures, including: S21, Set a blackbody radiation source, and place it under each blackbody temperature value in the temperature range information in sequence; S22, at each blackbody temperature value, ensures that the blackbody radiation source fills the field of view of the infrared thermal imager; S23, at each blackbody temperature value, the temperature of the blackbody radiation source is measured using an infrared thermal imager to obtain the temperature values ​​of all pixels of the infrared thermal imager; using the temperature values ​​of all pixels, a subset of measured temperatures corresponding to the blackbody temperature value is constructed. S24, using the temperature values ​​of all pixels of the infrared thermal imager obtained under all blackbody temperature values ​​of the blackbody radiation source, a set of measurement temperatures is constructed; the set of measurement temperatures includes a subset of measurement temperatures corresponding to each blackbody temperature value. S3, process the measured temperature set to obtain the calibration information of the infrared thermal imager, including: S31, for each subset of the measured temperatures in the set of measured temperatures, subtract the corresponding blackbody temperature value to obtain the corresponding subset of the difference temperatures; S32, preprocess all subsets of temperature differences to obtain a set of temperature differences, including: S321, Perform outlier removal on all differential temperature subsets to obtain the first differential temperature subset corresponding to each differential temperature subset; S322, filter each first differential temperature subset to obtain a differential temperature set; S33, perform correction and compensation processing on the differential temperature set and temperature value range information to obtain the temperature compensation value for all blackbody temperature values; S34, confirm the temperature compensation values ​​for all blackbody temperature values, which are the calibration information for the infrared thermal imager.

2. The refined calibration method for an infrared thermal imager as described in claim 1, characterized in that, The step of correcting and compensating the differential temperature set and temperature range information yields temperature compensation values ​​for all blackbody temperature values, including: S331, For each subset of the difference temperature set, perform joint difference region extraction to obtain the joint difference region; S332, calculate the difference feature value of the joint difference region to obtain the difference feature value of the difference temperature subset; S333, determine the difference characteristic value of the difference temperature subset, which is the temperature compensation value under the blackbody temperature value corresponding to the difference temperature subset; S334, for all subsets of differential temperatures, execute S331 to S333 to obtain the temperature compensation values ​​for all blackbody temperature values.

3. The refined calibration method for an infrared thermal imager as described in claim 2, characterized in that, The step of extracting a joint difference region for each subset of the difference temperature set to obtain a joint difference region includes: The first image information corresponding to each subset of temperature differences is uniformly divided to obtain several sub-images; Set the number of pixels n1 in the row direction and the number of pixels m1 in the column direction of the sub-image; Based on the first image information of each temperature difference subset, a difference region extraction model is constructed from all sub-images. The difference region extraction model is solved to obtain the joint difference region.

4. The refined calibration method for an infrared thermal imager as described in claim 3, characterized in that, The expression for the difference region extraction model is: ， In the formula, Represents the correlation quantity Parameters when taking the maximum value The value of , To determine the starting point coordinates of the first sub-image of the obtained joint difference region, The coordinates of the starting point of the first sub-image in the model are assigned. and These represent the pixel values ​​at coordinates [k+i, l+j] and [k+i+n1, l+j+m1] in the first image information, respectively. This represents the mean value of all pixels in the sub-image with starting point coordinates [k, l]. This represents the mean value of all pixels in the sub-image with starting point coordinates [k+n1, l+m1], where n and m are the row and column dimensions of the sub-image, respectively. This represents the average value of all pixels in the first image.

5. A fine calibration device for an infrared thermal imager, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the fine calibration method of the infrared thermal imager as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the fine calibration method for the infrared thermal imager as described in any one of claims 1 to 4.

7. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the refined calibration method for the infrared thermal imager as described in any one of claims 1 to 4.

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