Multi-point correction method, device and equipment of infrared imaging system and storage medium

Abstract

The application discloses a kind of multi-point correction method, device, equipment and storage medium of infrared imaging system. The method comprises: obtaining the exposure time of infrared imaging system and the image to be corrected;According to the multi-point correction reference data obtained in advance, determine the target time interval matched with the exposure time, and determine the temperature interval matched in each pixel of the image to be corrected;According to the temperature interval and the target time interval, determine the target reference data;According to the target reference data, determine the correction parameter of each pixel of the image to be corrected, to carry out non-uniformity correction to the image to be corrected. The technical scheme can solve the problem that the correction effect is not ideal under any integration time and any radiation scene, and can improve the correction effect while improving the adaptability of the correction scene.

Description

Technical Field

[0001] This invention relates to the field of infrared imaging technology, and in particular to a multi-point correction method, apparatus, device, and storage medium for an infrared imaging system. Background Technology

[0002] Non-uniformity in infrared imaging systems refers to the inconsistency in the output of different pixels when a uniform external radiation input is applied. Non-uniformity in infrared imaging systems severely affects the quality of infrared images. There are many non-uniformity correction techniques, which can be broadly categorized into two types: calibration-based and scene-based correction techniques. Calibration-based correction methods utilize uniform blackbody radiation at a set temperature as a reference source to calibrate the infrared focal plane array. Common calibration methods include one-point temperature calibration (single-point correction), two-point temperature calibration (two-point correction), and multi-point temperature calibration (multi-point correction). These calibration methods involve non-uniformity correction in the integration time dimension, i.e., fixing an integration time and analyzing only the direct relationship between the output response and the radiation temperature to obtain the gain and bias correction parameters. In reality, if the actual integration time of the infrared detector differs from the integration time used to obtain the correction parameters, the correction effect will deteriorate. This is mainly because the pixel response output exhibits a certain nonlinearity with the integration time.

[0003] Existing technologies typically calibrate infrared cameras at fixed integration times, pre-setting matching two-point temperature correction parameters for each integration time. However, this method only achieves good correction results within a given integration time, making it difficult to flexibly set the integration time. Additionally, some existing technologies propose that infrared imaging systems can obtain gain and bias correction coefficients corresponding to any integration time by fitting a linear relationship curve between the output response (or correction coefficient) and the integration time over a specific temperature range. However, while this approach allows for adjustable integration time, the formula transformation steps are cumbersome and it cannot adapt to wide temperature ranges and complex scenarios. Summary of the Invention

[0004] This invention provides a multi-point calibration method, apparatus, device, and storage medium for an infrared imaging system to solve the problem of unsatisfactory calibration results under arbitrary integration time and arbitrary radiation scenarios. It can improve the calibration effect while enhancing the adaptability of the calibration scenario.

[0005] According to one aspect of the present invention, a multi-point correction method for an infrared imaging system is provided, the method comprising:

[0006] Acquire the exposure time and the image to be corrected from the infrared imaging system;

[0007] Based on the pre-obtained multi-point correction reference data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined.

[0008] Based on the temperature range and the target time range, determine the target reference data;

[0009] Based on the target reference data, the correction parameters of each pixel in the image to be corrected are determined in order to perform non-uniformity correction on the image to be corrected.

[0010] According to another aspect of the present invention, a multi-point correction device for an infrared imaging system is provided, the device comprising:

[0011] The time and image acquisition module is used to acquire the exposure time and the image to be corrected from the infrared imaging system.

[0012] The interval determination module is used to determine the target time interval that matches the exposure time based on the pre-obtained multi-point correction reference data, and to determine the temperature interval that matches each pixel in the image to be corrected.

[0013] The target reference data determination module is used to determine target reference data based on the temperature range and the target time range;

[0014] The correction parameter determination module is used to determine the correction parameters of each pixel in the image to be corrected based on the target reference data, so as to perform non-uniformity correction on the image to be corrected.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the multi-point correction method of the infrared imaging system according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the multi-point correction method of the infrared imaging system according to any embodiment of the present invention.

[0020] The technical solution of this invention acquires the exposure time of an infrared imaging system and the image to be calibrated. Based on pre-obtained multi-point calibration reference data, it determines a target time interval matching the exposure time and a temperature interval matching each pixel in the image to be calibrated. Then, based on the temperature intervals and the target time interval, it determines target reference data and, based on the target reference data, determines the calibration parameters for each pixel in the image to be calibrated, thereby performing non-uniformity calibration on the image. This solution can solve the problem of unsatisfactory calibration results under arbitrary integration time and arbitrary radiation scenarios, improving both the calibration effect and the adaptability to different calibration scenarios.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a multi-point correction method for an infrared imaging system according to Embodiment 1 of the present invention;

[0024] Figure 2A This is a flowchart of a multi-point correction method for an infrared imaging system according to Embodiment 2 of the present invention;

[0025] Figure 2B This is a schematic diagram of the response curves under five calibration integration times provided in an embodiment of the present invention;

[0026] Figure 2C This is a schematic diagram comparing the corrected infrared images before and after adjusting the response saturation critical point according to an embodiment of the present invention.

[0027] Figure 3 This is a flowchart of a multi-point correction method for an infrared imaging system according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of a multi-point correction device for an infrared imaging system according to Embodiment 4 of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the multi-point correction method of the infrared imaging system according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0032] Example 1

[0033] Figure 1 This is a flowchart illustrating a multi-point correction method for an infrared imaging system according to Embodiment 1 of the present invention. This embodiment is applicable to image correction in any infrared imaging scenario. The method can be executed by a multi-point correction device of the infrared imaging system, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110: Acquire the exposure time and the image to be corrected from the infrared imaging system.

[0035] This solution can be executed by an infrared imaging system, which may include infrared detectors, infrared cameras, and other infrared imaging devices. The infrared imaging system can read the exposure time set by the infrared imaging devices.

[0036] After exposure, an infrared imaging device can output an image to be corrected. The image to be corrected can be an unprocessed infrared image output by the infrared imaging device, such as a 16-bit single-channel image output by an infrared detector.

[0037] S120. Based on the pre-obtained multi-point correction reference data, determine the target time interval that matches the exposure time, and determine the temperature interval that matches each pixel in the image to be corrected.

[0038] The infrared imaging system can predetermine multi-point calibration reference data, which may include at least four sets of calibration data. This calibration data may include blackbody temperature, integration time, and response image.

[0039] Specifically, an infrared imaging system can set multiple blackbody temperatures according to the applicable environment of the infrared imaging equipment. At each blackbody temperature, by setting different integration times, the system acquires response images output by the infrared imaging equipment at different blackbody temperatures and integration times. The blackbody temperatures and integration times can be set at equal intervals or with variable intervals. The infrared imaging system can define each pair of adjacent blackbody temperatures as a temperature interval and each pair of adjacent integration times as a time interval.

[0040] Infrared imaging systems can define the time interval to which the exposure time belongs as the target time interval. Based on the grayscale value of each pixel, the infrared imaging system can determine the blackbody radiant flux, and then, based on the direct proportionality between blackbody radiant flux and radiation temperature, determine the radiation temperature corresponding to each pixel. By comparing the radiation temperature corresponding to each pixel with the preset blackbody temperatures, the infrared imaging system can determine the temperature range matched to each pixel.

[0041] S130. Determine target reference data based on the temperature range and the target time range.

[0042] In this scheme, the target reference data can be the response image data from the calibration data. The infrared imaging system can determine the response grayscale value of each pixel in the image to be corrected at the endpoint of the target time interval based on the temperature range and the target time interval. Alternatively, the target reference data can be at least two sets of correction parameter data calculated using a multi-point correction parameter calculation formula based on at least four sets of calibration data. Based on the temperature range and the target time interval, the infrared imaging system can calculate the gain parameter and bias parameter corresponding to each pixel in the image to be corrected at the endpoint of the target time interval.

[0043] It should be noted that the time interval described in this solution can be an open interval, or an interval where the exposure time cannot be taken from the endpoints of the time interval. If the exposure time is one of the preset integration times, the infrared imaging system can directly obtain the correction parameters corresponding to the exposure time from the multi-point correction parameter data.

[0044] S140. Based on the target reference data, determine the correction parameters for each pixel of the image to be corrected, so as to perform non-uniformity correction on the image to be corrected.

[0045] Based on target reference data, such as the response grayscale values ​​of each pixel in the image to be corrected at the endpoints of the target time interval, or the gain and bias parameters corresponding to each pixel in the image to be corrected at the endpoints of the target time interval, the infrared imaging system can predict the response grayscale values ​​or correction parameters of each pixel in the image to be corrected using a prediction algorithm, or it can obtain the response grayscale values ​​or correction parameters of each pixel in the image to be corrected using an interpolation algorithm. Based on the response grayscale values ​​of each pixel in the image to be corrected, the infrared imaging system can calculate the correction parameters of each pixel according to the multi-point correction parameter formula. After obtaining the correction parameters, the infrared imaging system can use the correction parameters corresponding to each pixel to perform non-uniformity correction on the image to be corrected.

[0046] This technical solution acquires the exposure time of an infrared imaging system and the image to be calibrated. Based on pre-obtained multi-point calibration reference data, it determines the target time interval matching the exposure time and the temperature interval matching each pixel in the image to be calibrated. Then, based on the temperature interval and the target time interval, it determines the target reference data and, based on the target reference data, determines the calibration parameters for each pixel in the image to be calibrated, thereby performing non-uniformity correction on the image. This solution can solve the problem of unsatisfactory calibration results under arbitrary integration time and arbitrary radiation scenarios, improving both the calibration effect and the adaptability to different calibration scenarios.

[0047] Example 2

[0048] Figure 2A This is a flowchart of a training method for a multi-point correction model of an infrared imaging system according to Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Figure 2A As shown, the training method for the multi-point correction model of an infrared imaging system may include:

[0049] S210: Acquire the exposure time and the image to be corrected from the infrared imaging system.

[0050] S220. Based on the at least four sets of calibration data, determine the target time interval that matches the exposure time, and determine the temperature interval that matches each pixel in the image to be corrected.

[0051] In this scheme, the multi-point calibration reference data may include at least four sets of calibration data, which may include blackbody temperature, integration time, and response image. The at least four sets of calibration data may be obtained by calibration based on at least two blackbody temperatures and at least two integration times. Each pair of adjacent blackbody temperatures can constitute a temperature interval, and each pair of adjacent integration times can constitute a time interval.

[0052] Specifically, an infrared imaging system can represent the response image as a grayscale matrix using the grayscale values ​​of each pixel. The calibration data can be represented as a two-dimensional calibration matrix as shown in Table 1 below. In Table 1, InT1, InT2, etc., can represent different integration times; T1, T2, etc., can represent different blackbody temperatures; A11, A21, etc., can represent the grayscale matrix of the response image at each blackbody temperature and integration time. For example, A11 represents the grayscale matrix output by the infrared imaging device when the blackbody temperature is T1 and the integration time is InT1. In the two-dimensional calibration matrix shown in Table 1 below, two adjacent blackbody temperatures can form a temperature interval, such as the temperature interval (T1, T2). Similarly, two adjacent integration times can also form a time interval, such as the time interval (InT1, InT2).

[0053] Table 1:

[0054] InT1 InT2 InT3 InT4 …… T1 A11 A21 A31 A41 …… T2 A12 A22 A32 A42 …… T3 A13 A23 A33 A43 …… …… …… …… …… …… ……

[0055] Infrared imaging systems can define the time interval to which the exposure time belongs as the target time interval. Based on the grayscale value of each pixel, the infrared imaging system can determine the blackbody radiant flux, and then, based on the direct proportionality between blackbody radiant flux and radiation temperature, determine the radiation temperature corresponding to each pixel. By comparing the radiation temperature corresponding to each pixel with the preset blackbody temperatures, the infrared imaging system can determine the temperature range matched to each pixel.

[0056] S230. Determine response reference data based on the temperature range and the target time range.

[0057] Figure 2B This is a schematic diagram of the response curves at five calibration integration times provided according to an embodiment of the present invention. It is understood that... Figure 2B The response curve in blackbody mode is shown below. The infrared imaging system can statistically plot the response curves between two adjacent blackbody temperatures at various integration times based on calibration data. The horizontal axis of the response curve represents the blackbody temperature, and the vertical axis represents the response grayscale value. Figure 2B The numbers l1, l2, l3, and l4 in the equation can represent different temperature ranges.

[0058] like Figure 2BAs shown, based on the endpoints of the target time interval, for example, the endpoints of the target time interval (500µs, 800µs) are 500µs and 800µs, the infrared imaging system can locate the two response curves corresponding to the endpoints. Based on the response curves corresponding to the endpoints of the target time interval, and the temperature ranges matched to each pixel determined by S220, the infrared imaging system can determine two response curve segments, such as the response curve segment for temperature range l1 at an integration time of 500µs and the response curve segment for temperature range l1 at an integration time of 800µs. The infrared imaging system can determine two points in the two response curve segments based on the radiation temperature corresponding to each pixel, for example, the point shown in the response curve segments for a radiation temperature of 15 degrees at integration times of 500µs and 800µs. The infrared imaging system can use the response grayscale value of the target point in the two response curve segments as the response grayscale reference for that pixel, thereby obtaining the response reference data for the image to be corrected. The infrared imaging system can also use two response curve segments or two response curves as response reference data to satisfy the response grayscale reference for any pixel in the image to be corrected.

[0059] S240. Based on the response reference data, determine whether there is a response saturation critical point within the current temperature range.

[0060] After obtaining the response reference data, the infrared imaging system can verify the data to avoid response saturation within the temperature range, which could affect the determination of correction parameters. Specifically, the infrared imaging system can determine whether each response curve contains both periods of change in response grayscale values ​​and periods of no change in response grayscale values ​​within each temperature range, such as... Figure 2B As shown, at the position indicated by the arrow on the response curve corresponding to 2500µs, the response grayscale value no longer changes with temperature, indicating that response saturation has occurred starting from the position indicated by the arrow. Infrared imaging systems can determine response saturation by comparing the changes in response grayscale values ​​between adjacent temperature ranges during the target integration time. For example, by comparing the response grayscale values ​​of temperature range l3 and temperature range l4 during an integration time of 2500µs, it can be determined whether response saturation exists between the two temperature ranges.

[0061] S250. If it exists, calculate the correction parameter corresponding to the previous temperature range of the current temperature range, and use the correction parameter corresponding to the previous temperature range as the correction parameter data of the current temperature range to correct the response reference data.

[0062] If a response saturation critical point exists within the current temperature range, the infrared imaging system can calculate the correction parameters corresponding to the previous temperature range based on the calibration data. It then uses these correction parameters as the correction parameter data for the current temperature range, calculates the response grayscale values ​​at the endpoints of the current temperature range, and updates the response curve to correct the response reference data. For example... Figure 2B In the example, an infrared imaging system can use the correction parameters for temperature range l2 as the correction parameters for temperature range l3. This involves extending the response curve of temperature range l2 into temperature range l3 and shifting the response saturation critical point to the endpoint of the current temperature range to ensure consistent correction parameters within the same temperature range. By correcting the response reference data, the infrared imaging system can adjust the response saturation critical point to avoid affecting image correction quality.

[0063] Figure 2C This is a schematic diagram comparing the corrected infrared images before and after adjusting the response saturation critical point according to an embodiment of the present invention, as shown below. Figure 2C A comparison of infrared images before and after adjusting the response saturation critical point shows that this solution can effectively improve the response saturation phenomenon and enhance image quality.

[0064] S260. Based on the response reference data, calculate the correction parameters for each pixel of the image to be corrected using linear interpolation.

[0065] Specifically, in this scheme, the step of calculating the correction parameters of each pixel in the image to be corrected through linear interpolation based on the response reference data includes:

[0066] Based on the response reference data, response data matching the exposure time is calculated by linear interpolation;

[0067] Based on the response data, the correction parameters for each pixel of the image to be corrected are calculated.

[0068] Infrared imaging systems can calculate response grayscale data matching the exposure time using linear interpolation based on response reference data. Specifically, the formula for linear interpolation is:

[0069]

[0070] Where r represents the interpolation point, A(r) represents the interpolation result mapped from the interpolation point r, p and q represent the endpoints of the interval where the interpolation point is located, and A(p) and A(q) represent the known mapping values ​​of each interval endpoint.

[0071] Furthermore, the infrared imaging system can calculate the correction parameters for each pixel of the image to be corrected using a multi-point correction parameter calculation formula based on the response grayscale data, in order to perform non-uniformity correction on the image. For example, the response reference data includes response curves at an integration time of 500µs and 800µs. Through linear interpolation, the infrared imaging system can calculate the response curve at an exposure time of 600µs. Based on the response curve at an exposure time of 600µs, the correction parameters for each temperature range can be calculated. The infrared imaging system can then perform non-uniformity correction on the image to be corrected based on the correction parameters matched to each pixel.

[0072] This technical solution acquires the exposure time and the image to be calibrated from an infrared imaging system. Based on multi-point calibration data, it determines response reference data that matches the exposure time and the image to be calibrated. Then, based on the response reference data, it calculates the correction parameters for each pixel in the image to be calibrated through linear interpolation to perform non-uniformity correction. This solution can solve the problem of unsatisfactory correction results under arbitrary integration time and arbitrary radiation scenarios, improving both the correction effect and the adaptability to different correction scenarios. Furthermore, this solution can also correct the response reference data through a response saturation check, effectively avoiding response saturation during the calibration process.

[0073] Example 3

[0074] Figure 3 This is a flowchart of a training method for a multi-point correction model of an infrared imaging system according to Embodiment 3 of the present invention. This embodiment is a refinement based on the above embodiment. Figure 3 As shown, the training method for the multi-point correction model of an infrared imaging system may include:

[0075] S310: Acquire the exposure time and the image to be corrected from the infrared imaging system.

[0076] S320. Based on the at least four sets of calibration data, determine the target time interval that matches the exposure time, and determine the temperature interval that matches each pixel in the image to be corrected.

[0077] Based on the calibration data described in S220, the infrared imaging system can calculate at least two sets of correction parameter data from at least four sets of calibration data. These correction parameter data may include gain parameters and bias parameters.

[0078] The easily understood correction model within the temperature range l of multi-point correction can be expressed as:

[0079] y i,j (T)=G i,j (l)x i,j (T)+O i,j(l);

[0080] Where T represents any temperature point within the temperature range l, x i,j (T) represents the output grayscale value of the i-th row and j-th column pixel at temperature T, G i,j (l) represents the gain parameter for temperature range l, O i,j (l) represents the bias parameter within the temperature range l, y i,j (T l ) represents the output grayscale value of the i-th row and j-th column pixel of the image after correction at temperature T.

[0081] The gain parameter G i,j The formula for calculating (l) can be expressed as:

[0082]

[0083] The bias parameter O i,j The formula for calculating (l) can be expressed as:

[0084]

[0085] in, The blackbody temperature T is represented by l The average output grayscale value of each pixel in the response image.

[0086] Based on the above calculation formulas for gain and bias parameters, the infrared imaging system can calculate the gain and bias parameters for each temperature range, and thus obtain the two-dimensional parameter matrix shown in Table 2 below.

[0087] Table 2:

[0088] InT1 InT2 InT3 …… (T1-T2) G11, O11 G21, O21 G31, O31 …… (T2-T3) G12, O12 G22, O22 G32, O32 …… (T3-T4) G13, O13 G23, O23 G33, O33 …… …… …… …… …… ……

[0089] Among them, G11, G21, etc. can represent the gain parameter matrix of the response image under each temperature range, and O11, O21, etc. can represent the bias parameter matrix of the response image under each temperature range.

[0090] S330. Determine the correction reference data based on the temperature range and the target time range.

[0091] Infrared imaging systems can compare exposure time with various integration times to determine the target time interval to which the exposure time belongs. For example... Figure 2BAs shown, the preset integration times include 500µs, 800µs, 1500µs, 2000µs, and 2500µs. These five integration times can form four time intervals: (500µs, 800µs), (800µs, 1500µs), (1500µs, 2000µs), and (2000µs, 2500µs). Assuming an exposure time of 600µs, the target time interval for the exposure time matching can be (500µs, 800µs). It is understandable that, similar to the response image, the image to be corrected can also be represented as a grayscale matrix based on the grayscale values ​​of each pixel. Based on the grayscale values ​​of each pixel, the infrared imaging system can determine the blackbody radiant flux, and then, based on the direct proportionality between the blackbody radiant flux and the radiation temperature, determine the radiation temperature corresponding to each pixel. Based on the comparison between the radiation temperature corresponding to each pixel and the preset blackbody temperatures, the infrared imaging system can determine the temperature interval matching each pixel, and then determine the gain parameter and bias parameter corresponding to that temperature interval in the correction parameter data.

[0092] S340. Based on the correction reference data, calculate the correction parameters of each pixel in the image to be corrected by linear interpolation.

[0093] As is easily understood, an infrared imaging system can determine the target integration time based on the endpoints of the target time interval, and then filter out the correction parameter data associated with the target integration time from the correction parameter data. Based on the target integration time and the correction parameter data associated with it, a correction parameter matching the exposure time is calculated through linear interpolation. Similarly, the linear interpolation calculation formula can be as described in S260.

[0094] The target integration time can include the integration times corresponding to the endpoints of two intervals. For example, the first integration time can represent the integration time corresponding to the left endpoint of the interval, and the second integration time can represent the integration time corresponding to the right endpoint of the interval. The infrared imaging system can filter out the gain and bias parameters for each temperature interval corresponding to the first and second integration times from the calibration parameter data. The infrared imaging system can then perform linear interpolation on the calibration parameters corresponding to the first and second integration times to obtain the calibration parameters corresponding to the exposure time.

[0095] Using the linear interpolation formula described above, and substituting the gain parameters of the first and second integration times, the infrared imaging system can calculate the gain parameters for each temperature range under the exposure time. Similarly, by substituting the bias parameters of the first and second integration times, the infrared imaging system can calculate the bias parameters for each temperature range under the exposure time.

[0096] It should be noted that, similar to S240, after determining the calibration reference data, this scheme allows the infrared imaging system to plot response curves at each integration time based on the calibration data, and to perform a response saturation test based on the response data at each integration time and within each temperature range. The infrared imaging system can shift the saturation critical point when response saturation occurs within the temperature range.

[0097] This technical solution acquires the exposure time of an infrared imaging system and the image to be calibrated. Based on multi-point calibration reference data, it determines calibration reference data that matches the exposure time and the image to be calibrated. Then, based on the calibration reference data, it determines the calibration parameters for each pixel in the image to be calibrated, thereby performing non-uniformity correction on the image. This solution can solve the problem of unsatisfactory calibration results under arbitrary integration time and arbitrary radiation scenarios, improving both the calibration effect and the adaptability to different calibration scenarios. Furthermore, this solution can also correct the calibration reference data through a response saturation test, effectively avoiding response saturation during the calibration process.

[0098] Example 4

[0099] Figure 4 This is a schematic diagram of the structure of a multi-point correction device for an infrared imaging system provided in Embodiment 4 of the present invention. Figure 4 As shown, the device includes:

[0100] The time and image acquisition module 410 is used to acquire the exposure time and the image to be corrected from the infrared imaging system.

[0101] The interval determination module 420 is used to determine the target time interval that matches the exposure time based on the pre-obtained multi-point correction reference data, and to determine the temperature interval that matches each pixel in the image to be corrected.

[0102] The target reference data determination module 430 is used to determine target reference data based on the temperature range and the target time range;

[0103] The correction parameter determination module 440 is used to determine the correction parameters of each pixel in the image to be corrected based on the target reference data, so as to perform non-uniformity correction on the image to be corrected.

[0104] In one feasible scheme, the multi-point calibration reference data includes at least four sets of calibration data; the calibration data includes blackbody temperature, integration time, and response image; wherein, the at least four sets of calibration data are obtained by calibration based on at least two blackbody temperatures and at least two integration times; each pair of adjacent blackbody temperatures constitutes a temperature interval, and each pair of adjacent integration times constitutes a time interval.

[0105] The interval determination module 420 is specifically used for:

[0106] Based on the at least four sets of calibration data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined.

[0107] The target reference data determination module 430 is specifically used for:

[0108] Based on the temperature range and the target time range, determine the response reference data;

[0109] The correction parameter determination module 440 is specifically used for:

[0110] Based on the response reference data, the correction parameters for each pixel of the image to be corrected are determined.

[0111] In another feasible scheme, the multi-point calibration reference data includes at least four sets of calibration data and at least two sets of calibration parameter data; wherein, the at least four sets of calibration data are obtained by calibration based on at least two blackbody temperatures and at least two integration times; each pair of adjacent blackbody temperatures constitutes a temperature interval, and each pair of adjacent integration times constitutes a time interval; the at least two sets of calibration parameter data are determined based on the at least four sets of calibration data.

[0112] The interval determination module 420 is specifically used for:

[0113] Based on the at least four sets of calibration data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined.

[0114] The target reference data determination module 430 is specifically used for:

[0115] Based on the temperature range and the target time range, determine the correction reference data;

[0116] The correction parameter determination module 440 is specifically used for:

[0117] Based on the correction reference data, the correction parameters for each pixel in the image to be corrected are determined.

[0118] Based on the above scheme, the correction parameter determination module 440 is specifically used for:

[0119] Based on the response reference data, the correction parameters for each pixel of the image to be corrected are calculated by linear interpolation.

[0120] Optionally, the correction parameter determination module 440 is specifically used for:

[0121] Based on the correction reference data, the correction parameters of each pixel in the image to be corrected are calculated by linear interpolation.

[0122] In a preferred embodiment, the device further includes:

[0123] The first saturation judgment module is used to determine whether there is a response saturation critical point within the current temperature range based on the response reference data.

[0124] The response reference data correction module is used to calculate the correction parameters corresponding to the previous temperature range of the current temperature range if they exist, and use the correction parameters corresponding to the previous temperature range as the correction parameter data of the current temperature range to correct the response reference data.

[0125] Based on the above scheme, the correction parameter determination module 440 is specifically used for:

[0126] Based on the response reference data, response data matching the exposure time is calculated by linear interpolation;

[0127] Based on the response data, the correction parameters for each pixel of the image to be corrected are calculated.

[0128] The multi-point correction device for the infrared imaging system provided in this embodiment of the invention can execute the multi-point correction method for the infrared imaging system provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0129] Example 5

[0130] Figure 5 A schematic diagram of an electronic device 510 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0131] like Figure 5As shown, the electronic device 510 includes at least one processor 511 and a memory, such as a read-only memory (ROM) 512 or a random access memory (RAM) 513, communicatively connected to the at least one processor 511. The memory stores computer programs executable by the at least one processor. The processor 511 can perform various appropriate actions and processes based on the computer program stored in the ROM 512 or loaded into the RAM 513 from storage unit 518. The RAM 513 may also store various programs and data required for the operation of the electronic device 510. The processor 511, ROM 512, and RAM 513 are interconnected via a bus 514. An input / output (I / O) interface 515 is also connected to the bus 514.

[0132] Multiple components in electronic device 510 are connected to I / O interface 515, including: input unit 516, such as keyboard, mouse, etc.; output unit 517, such as various types of displays, speakers, etc.; storage unit 518, such as disk, optical disk, etc.; and communication unit 519, such as network card, modem, wireless transceiver, etc. Communication unit 519 allows electronic device 510 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0133] Processor 511 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 511 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 511 performs the various methods and processes described above, such as the multi-point correction method in an infrared imaging system.

[0134] In some embodiments, the multi-point calibration method of the infrared imaging system may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 518. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 510 via ROM 512 and / or communication unit 519. When the computer program is loaded into RAM 513 and executed by processor 511, one or more steps of the multi-point calibration method of the infrared imaging system described above may be performed. Alternatively, in other embodiments, processor 511 may be configured to perform the multi-point calibration method of the infrared imaging system by any other suitable means (e.g., by means of firmware).

[0135] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0137] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0139] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0140] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0141] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0142] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-point calibration method for an infrared imaging system, characterized in that, The method includes: Acquire the exposure time and the image to be corrected from the infrared imaging system; Based on the pre-obtained multi-point correction reference data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined; wherein, determining the temperature interval matching each pixel in the image to be corrected includes: determining the corresponding blackbody radiant flux based on the gray value of each pixel in the image to be corrected, determining the independent radiant temperature of each pixel based on the direct proportionality between blackbody radiant flux and radiant temperature, and determining the temperature interval matching each pixel based on the comparison result between the radiant temperature corresponding to each pixel and the preset blackbody temperature. Based on the temperature range and the target time range, determine the target reference data; Based on the target reference data, the correction parameters of each pixel in the image to be corrected are determined in order to perform non-uniformity correction on the image to be corrected. The multi-point calibration reference data includes at least four sets of calibration data; wherein the at least four sets of calibration data are obtained by calibration based on at least two blackbody temperatures and at least two integration times; each pair of adjacent blackbody temperatures constitutes a temperature interval, and each pair of adjacent integration times constitutes a time interval. The step of determining the target time interval matching the exposure time based on pre-obtained multi-point correction reference data, and determining the temperature interval matching each pixel in the image to be corrected, includes: Based on the at least four sets of calibration data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined. The step of determining the target reference data based on the temperature range and the target time range includes: Based on the temperature range and the target time range, determine the response reference data; The step of determining the correction parameters for each pixel of the image to be corrected based on the target reference data includes: Based on the response reference data, the correction parameters for each pixel of the image to be corrected are determined; The calibration data may include blackbody temperature, integration time, and response image. The infrared imaging system sets multiple blackbody temperatures according to the applicable environment of the infrared imaging device. At each blackbody temperature, by setting different integration times, the response images output by the infrared imaging device at different blackbody temperatures and different integration times are obtained. Before determining the correction parameters for each pixel of the image to be corrected based on the response reference data, the method further includes: Based on the response reference data, determine whether there is a response saturation critical point within the current temperature range; If it exists, calculate the correction parameter corresponding to the previous temperature range of the current temperature range, and use the correction parameter corresponding to the previous temperature range as the correction parameter data of the current temperature range to correct the response reference data. The step of using the correction parameters corresponding to the previous temperature range as the correction parameter data for the current temperature range includes: directly extending the response curve of the previous temperature range to the current temperature range, and forcibly moving the saturation critical point to the endpoint of the current temperature range.

2. The method according to claim 1, characterized in that, The multi-point calibration reference data includes at least four sets of calibration data and at least two sets of calibration parameter data; wherein, the at least four sets of calibration data are obtained by calibration based on at least two blackbody temperatures and at least two integration times; each pair of adjacent blackbody temperatures constitutes a temperature interval, and each pair of adjacent integration times constitutes a time interval; the at least two sets of calibration parameter data are determined based on at least four sets of calibration data. The step of determining the target time interval matching the exposure time based on pre-obtained multi-point correction reference data, and determining the temperature interval matching each pixel in the image to be corrected, includes: Based on the at least four sets of calibration data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined. The step of determining the target reference data based on the temperature range and the target time range includes: Based on the temperature range and the target time range, determine the correction reference data; The step of determining the correction parameters for each pixel of the image to be corrected based on the target reference data includes: Based on the correction reference data, the correction parameters for each pixel in the image to be corrected are determined.

3. The method according to claim 1, characterized in that, The step of determining the correction parameters for each pixel of the image to be corrected based on the response reference data includes: Based on the response reference data, the correction parameters for each pixel of the image to be corrected are calculated by linear interpolation.

4. The method according to claim 2, characterized in that, The step of determining the correction parameters for each pixel of the image to be corrected based on the correction reference data includes: Based on the correction reference data, the correction parameters of each pixel in the image to be corrected are calculated by linear interpolation.

5. The method according to claim 3, characterized in that, The step of calculating the correction parameters of each pixel in the image to be corrected by linear interpolation based on the response reference data includes: Based on the response reference data, response data matching the exposure time is calculated by linear interpolation; Based on the response data, the correction parameters for each pixel of the image to be corrected are calculated.

6. A multi-point correction device for an infrared imaging system, characterized in that, include: The time and image acquisition module is used to acquire the exposure time and the image to be corrected from the infrared imaging system. The interval determination module is used to determine the target time interval matching the exposure time based on the pre-obtained multi-point correction reference data, and to determine the temperature interval matching each pixel in the image to be corrected; wherein, determining the temperature interval matching each pixel in the image to be corrected includes: determining the corresponding blackbody radiant flux based on the gray value of each pixel in the image to be corrected, determining the independent radiant temperature of each pixel based on the direct proportionality between blackbody radiant flux and radiant temperature, and determining the temperature interval matching each pixel based on the comparison result between the radiant temperature corresponding to each pixel and the preset blackbody temperature; The target reference data determination module is used to determine target reference data based on the temperature range and the target time range; The correction parameter determination module is used to determine the correction parameters of each pixel in the image to be corrected based on the target reference data, so as to perform non-uniformity correction on the image to be corrected. The multi-point calibration reference data includes at least four sets of calibration data and at least two sets of calibration parameter data; wherein the at least four sets of calibration data are obtained by calibration based on at least two blackbody temperatures and at least two integration times; each pair of adjacent blackbody temperatures constitutes a temperature interval, and each pair of adjacent integration times constitutes a time interval; the at least two sets of calibration parameter data are determined based on at least four sets of calibration data. The interval determination module is specifically used for: Based on the at least four sets of calibration data, a target time interval matching the exposure time is determined, and a temperature interval matching each pixel in the image to be corrected is determined. The target reference data determination module is specifically used for: Based on the temperature range and the target time range, determine the correction reference data; The correction parameter determination module is specifically used for: Based on the correction reference data, determine the correction parameters for each pixel of the image to be corrected; The calibration data may include blackbody temperature, integration time, and response image. The infrared imaging system sets multiple blackbody temperatures according to the applicable environment of the infrared imaging device. At each blackbody temperature, by setting different integration times, the response images output by the infrared imaging device at different blackbody temperatures and different integration times are obtained. The device further includes: The first saturation judgment module is used to determine whether there is a response saturation critical point within the current temperature range based on the response reference data. The response reference data correction module is used to calculate the correction parameters corresponding to the previous temperature range of the current temperature range if they exist, and use the correction parameters corresponding to the previous temperature range as the correction parameter data of the current temperature range to correct the response reference data. The step of using the correction parameters corresponding to the previous temperature range as the correction parameter data for the current temperature range includes: directly extending the response curve of the previous temperature range to the current temperature range, and forcibly moving the saturation critical point to the endpoint of the current temperature range.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-point correction method of the infrared imaging system according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multi-point correction method of the infrared imaging system according to any one of claims 1-5.

Images