Anti-glare automatic imaging control method and system for infrared imaging

Abstract

This invention relates to the technical field of infrared imaging, specifically to an automatic imaging control method and system for infrared imaging to combat stray light. The method includes: acquiring image data and corresponding stray light data; determining the stray light imaging influence degree based on device temperature time-series data and light intensity time-series data; correcting the stray light imaging influence degree based on dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the comprehensive probability of strong stray light interference in the infrared image; determining the comprehensive stray light resistance stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared image; determining the stray light suppression urgency based on the comprehensive stray light resistance stability and the strong stray light tendency coefficient; and determining the target imaging control mode based on the stray light suppression urgency and a preset threshold, so that the infrared imager performs adaptive imaging control according to the target imaging control mode. This invention can improve the accuracy of stray light imaging control during infrared imaging.

Description

Technical Field

[0001] This invention relates to the technical field of infrared imaging, and specifically to an automatic imaging control method and system for infrared imaging with anti-stray light. Background Technology

[0002] Infrared imaging is a technology that converts and presents the infrared radiation emitted by an object. Any substance with a temperature above absolute zero (0K, -273.15°C) will continuously radiate infrared radiation. Therefore, infrared imaging technology converts thermal radiation intensity into electrical signals, which are then processed to generate thermal images for application.

[0003] Strong stray light interference may occur during the imaging process of infrared imagers, such as environmental reflection, internal thermal radiation of the system, and scattering of optical elements, which reach the detector image plane and cause a decrease in the signal-to-noise ratio of the infrared image. To address this phenomenon, the control mode of the infrared imager is often switched, specifically a high-resolution mode and a low stray light mode. When the stray light interference is strong, the low stray light mode is switched to, sacrificing resolution to ensure the realism of the image. Conversely, when the stray light interference is weak, it can be directly switched to the relatively high-definition narrow slit high-resolution mode.

[0004] Currently, when assessing the necessity of switching to a low stray light mode during infrared imaging, the traditional approach relies solely on threshold judgment based on real-time light intensity levels. When the threshold is exceeded, the mode is switched to low stray light mode. However, in real-world scenarios, light intensity changes are complex, and factors such as dust and equipment temperature can cause light scattering, increasing stray light interference. This results in insufficient accuracy of the results obtained by relying solely on real-time light intensity level analysis, reducing the precision of anti-stray light imaging control during infrared imaging. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic imaging control method and system for infrared imaging with anti-stray light, which solves the technical problem that the accuracy of the results obtained by relying solely on real-time light intensity level analysis in the prior art is low, resulting in low accuracy of anti-stray light imaging control during the infrared imaging process.

[0006] In a first aspect, one embodiment of the present invention provides an automatic imaging control method for infrared imaging with anti-stray light, the method comprising:

[0007] Acquire image data and corresponding stray light data; stray light data includes: device temperature time-series data, light intensity time-series data, and dust particle concentration data; image data includes RGB original image and infrared image.

[0008] Based on the time-series data of equipment temperature and light intensity, the influence of stray light imaging is determined; the influence of stray light imaging is used to characterize the basic stray light interference in infrared images.

[0009] The influence of stray light on imaging is corrected based on dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the overall probability of strong stray light interference in infrared images;

[0010] The overall anti-stray light stability is determined based on the texture difference of image data and the radiometric stability value corresponding to the infrared image; the texture difference is used to characterize the degree of structural consistency difference between the infrared image and the original RGB image.

[0011] The urgency of stray light suppression is determined based on the comprehensive stray light stability and the strong stray light tendency coefficient.

[0012] Based on the urgency of stray light suppression and a preset threshold, the target imaging control mode is determined so that the infrared imager can perform adaptive imaging control according to the target imaging control mode.

[0013] In one embodiment, determining the stray light imaging impact based on device temperature time-series data and light intensity time-series data includes:

[0014] Determine the slope of temperature change based on equipment temperature time-series data;

[0015] The high temperature trend factor is determined based on the slope of temperature change, real-time temperature value, and historical maximum temperature value; the high temperature trend factor is used to characterize stray light interference caused by internal thermal radiation of the equipment.

[0016] Based on time-series light intensity data, the standard deviation of light intensity signal fluctuation is determined;

[0017] The light intensity imaging superiority is determined based on the standard deviation of the light intensity signal fluctuation, the real-time light intensity value, and the historical light intensity mean; the light intensity imaging superiority is used to characterize external stray light interference caused by ambient light.

[0018] The influence of stray light on imaging is determined based on the high temperature tendency factor and the light intensity imaging priority.

[0019] In one embodiment, the step of correcting the stray light imaging influence based on dust particle concentration data to obtain a strong stray light tendency coefficient includes:

[0020] Obtain the historical average dust particle concentration of the infrared imager;

[0021] The correction factor is determined based on real-time dust particle concentration data and historical average dust particle concentration.

[0022] The influence of stray light on imaging is corrected based on the correction factor to obtain the strong stray light tendency coefficient.

[0023] In one embodiment, the infrared image includes a first infrared image corresponding to a low stray light mode and a second infrared image corresponding to a high-resolution mode. The determination of overall stray light resistance stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared image includes:

[0024] In the low stray light mode, the first texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original RGB image and the first infrared image.

[0025] Based on the radiation response values ​​of the same corner points of the first infrared images in the control analysis group, a first radiation stability value is determined; the first radiation stability value is used to characterize the radiation intensity stability of the first infrared images in a short time series.

[0026] A first comprehensive anti-stray light stability is determined based on a first texture difference degree and a first radiation stability value; the first comprehensive anti-stray light stability is used to characterize the comprehensive ability of a first infrared image to resist stray light interference in a low stray light mode.

[0027] In one embodiment, determining the overall anti-stray light stability based on the texture difference of image data and the radiometric stability value corresponding to the infrared image includes:

[0028] In high-resolution mode, the second texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original RGB image and the second infrared image.

[0029] The second radiation stability value is determined based on the radiation response values ​​of the same corner points of the second infrared images in the control analysis group; the second radiation stability value is used to characterize the radiation intensity stability of the second infrared images in a short time series.

[0030] The second comprehensive anti-stray light stability is determined based on the second texture difference degree and the second radiation stability value; the second comprehensive anti-stray light stability is used to characterize the comprehensive ability of the second infrared image to resist stray light interference in high resolution mode.

[0031] In one embodiment, determining the urgency of stray light suppression based on the comprehensive stray light stability and strong stray light tendency coefficient includes:

[0032] Based on the absolute value of the difference between the first comprehensive anti-stray light stability and the second comprehensive anti-stray light stability;

[0033] The urgency of stray light suppression is determined based on the absolute value of the difference and the strong stray light tendency coefficient.

[0034] In one embodiment, determining the target imaging control mode based on the urgency of stray light suppression and a preset threshold includes:

[0035] When the urgency of stray light suppression is greater than a preset threshold, the target imaging control mode is determined to be the low stray light mode;

[0036] When the urgency of stray light suppression is less than or equal to a preset threshold, the target imaging control mode is determined to be the high-resolution mode.

[0037] In one embodiment, after determining the target imaging control mode based on the urgency of stray light suppression and a preset threshold, the method further includes:

[0038] For a target with known radiation intensity, the gain and bias coefficient of each pixel in the infrared image are determined, and the non-uniformity correction of the infrared image is performed by a two-point correction method.

[0039] The bad pixels / blind pixels in the infrared image are marked and detected by the bad pixel map, and the bad pixels / blind pixels are replaced by directional interpolation to obtain the repaired infrared image.

[0040] Secondly, another embodiment of the present invention provides an anti-stray light automatic imaging control system for infrared imaging, the system comprising:

[0041] The acquisition module is used to acquire image data and stray light data corresponding to the image data; the stray light data includes: device temperature time series data, light intensity time series data, and dust particle concentration data; the image data includes RGB original image and infrared image.

[0042] The determination module is used to determine the stray light imaging influence degree based on the device temperature time series data and light intensity time series data; the stray light imaging influence degree is used to characterize the basic stray light interference of infrared images;

[0043] The module is used to correct the influence of stray light on imaging based on dust particle concentration data, and obtain the strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the overall probability of strong stray light interference in infrared images;

[0044] The determination module is also used to determine the overall anti-stray light stability based on the texture difference degree of the image data and the radiometric stability value corresponding to the infrared image; the texture difference degree is used to characterize the degree of structural consistency difference between the infrared image and the original RGB image; the urgency of stray light suppression is determined according to the overall anti-stray light stability and the strong stray light tendency coefficient; and the target imaging control mode is determined according to the urgency of stray light suppression and the preset threshold, so that the infrared imager can perform adaptive imaging control according to the target imaging control mode.

[0045] In one embodiment, the determining module includes:

[0046] The module determines the temperature change slope based on the equipment's temperature time-series data; it also determines the high-temperature trend factor based on the temperature change slope, real-time temperature value, and historical maximum temperature value; the high-temperature trend factor characterizes stray light interference caused by internal thermal radiation of the equipment; it determines the standard deviation of light intensity fluctuation based on light intensity time-series data; it determines the light intensity imaging dominance based on the standard deviation of light intensity fluctuation, real-time light intensity value, and historical average light intensity value; the light intensity imaging dominance characterizes external stray light interference caused by ambient light; and it determines the stray light imaging influence degree based on the high-temperature trend factor and the light intensity imaging dominance degree.

[0047] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0048] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0049] The present invention has the following beneficial effects:

[0050] In the process of automatic imaging control against stray light, this embodiment of the invention first acquires image data and stray light data corresponding to the image data; based on the time-series data of device temperature and light intensity, the influence degree of stray light imaging is determined; the influence degree of stray light imaging is corrected based on dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the comprehensive probability of strong stray light interference in the infrared image; based on the texture difference degree of the image data and the radiation stability value corresponding to the infrared image, the comprehensive anti-stray light stability is determined; based on the comprehensive anti-stray light stability and the strong stray light tendency coefficient, the urgency of stray light suppression is determined; based on the urgency of stray light suppression and a preset threshold, the target imaging control mode is determined so that the infrared imager performs adaptive imaging control according to the target imaging control mode. This invention can automatically select the imaging control mode of an infrared imager based on the stray light interference behavior during infrared imaging. Therefore, it first analyzes the stray light tendency coefficient based on the stray light tendency behavior reflected by the high temperature of the equipment and the external environment, and then obtains the stray light suppression urgency parameter by combining the stray light effect feedback of real-time infrared imaging. Based on the real-time stray light condition performance reflected by the stray light suppression urgency parameter, the target imaging control mode of the infrared imager is determined, thereby improving the anti-stray light imaging control accuracy during infrared imaging. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0052] Figure 1 This is a schematic flowchart of an automatic imaging control method for infrared imaging with anti-stray light provided in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of an anti-stray light automatic imaging control system for infrared imaging provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0055] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic imaging control method and system for infrared imaging with anti-stray light proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0057] The following describes in detail, with reference to the accompanying drawings, a specific scheme of the automatic imaging control method for infrared imaging with anti-stray light provided by the present invention.

[0058] This invention proposes an automatic imaging control method for infrared imaging with anti-stray light. Please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a schematic flowchart of an automatic imaging control method for infrared imaging with anti-stray light according to an embodiment of the present invention. The method includes the following steps:

[0059] Step S1: Obtain image data and stray light data corresponding to the image data; stray light data includes: device temperature time series data, light intensity time series data, and dust particle concentration data; image data includes RGB original image and infrared image.

[0060] Image data refers to two types of image data collected for analyzing stray light interference and determining imaging modes. It forms the basis for subsequent texture comparison and stability analysis, and specifically includes RGB original images and infrared images.

[0061] Stray light data refers to data directly related to stray light interference during infrared imaging. It is used to quantify the source (equipment itself, external environment) and intensity of stray light interference, and is used to calculate the stray light imaging impact degree and strong stray light tendency coefficient.

[0062] For example, stray light data may include: device temperature time series data, light intensity time series data, and dust particle concentration data.

[0063] Equipment temperature time series data refers to the dataset formed by arranging the continuous temperature data within a short period of 3 seconds during the operation of the infrared imager, which is collected by temperature sensors, in chronological order. It reflects the temperature change trend of the equipment itself, such as the temperature fluctuations caused by energy loss of core components (lens tube, detector dewar flask, etc.). High temperature of the equipment will aggravate internal thermal radiation, thereby increasing stray light interference.

[0064] Light intensity time series data refers to the continuous light intensity signals within a short period of 3 seconds near the imager, collected by the light intensity sensing module and organized according to the time series. It reflects the intensity and fluctuation of ambient light, such as the changes in infrared radiation intensity of strong light sources like sunlight. Strong light or drastic fluctuations in light intensity can exacerbate external stray light interference.

[0065] Dust particle concentration data refers to the real-time dust particle concentration value near the imager collected by the particulate matter concentration sensing module. Dust particles (especially those with a particle size similar to the infrared wavelength) scatter infrared light, increasing stray light interference.

[0066] Red-Green-Blue (RGB) original images refer to the original images generated based on the three primary color imaging principle (capturing the reflection of visible light by objects). They serve as a scene benchmark for infrared images and are used to compare and analyze the damage of stray light to the texture structure of infrared imaging.

[0067] Infrared images refer to images generated based on the infrared radiation emitted by an object itself (objects with a temperature above absolute zero will radiate infrared radiation). They include images with two parallel processing modes: low stray light mode infrared images (focusing on stray light suppression and noise reduction) and high resolution mode infrared images (focusing on detail enhancement).

[0068] It should be noted that in the process of anti-stray light automatic imaging control of the present invention, image data and stray light data need to be acquired simultaneously. The stray light data is used to quantify the source and intensity of stray light interference, and the image data is used for texture comparison and stability analysis.

[0069] Step S2: Determine the stray light imaging influence degree based on the equipment temperature time series data and light intensity time series data; the stray light imaging influence degree is used to characterize the basic stray light interference of infrared images.

[0070] Among them, the stray light imaging impact degree refers to a parameter calculated by combining the time-series data of equipment temperature and light intensity. It is used to quantify the degree of influence of the combined effect of high equipment temperature and ambient light intensity on stray light interference during infrared imaging. The higher the value, the greater the possibility that high equipment temperature and light intensity will jointly cause strong stray light interference. It is the core indicator characterizing the basic stray light interference of infrared images.

[0071] Basic stray light interference refers to stray light interference caused by core factors such as thermal radiation generated by the high temperature of the equipment itself and changes in ambient light intensity during infrared imaging, excluding the stray light effects superimposed by dust particle scattering and other subsequent superpositions.

[0072] For example, in an embodiment of the present invention, the device temperature time series data and ambient light intensity time series data within a short period of 3 seconds are first collected by the corresponding sensing module, and then the stray light imaging influence degree is calculated based on these two types of data; the stray light imaging influence degree is used to quantify the degree of basic stray light interference generated by the combined effect of high device temperature and ambient light intensity.

[0073] Furthermore, determining the stray light imaging influence based on device temperature time-series data and light intensity time-series data includes:

[0074] The slope of temperature change is determined based on the time-series temperature data of the equipment.

[0075] The temperature change slope refers to the slope (denoted as K) of the straight line obtained by fitting the sample points in a two-dimensional coordinate system with the time sequence of equipment temperature data as the x-axis and the corresponding temperature values ​​as the y-axis. This slope intuitively reflects the rate of change of equipment temperature within a specific time period. The steeper the slope, the faster the equipment temperature rises or falls, and it is used to quantify the trend of equipment temperature change.

[0076] For example, the process of determining the slope of temperature change is based on the temperature data of the infrared imager device collected in chronological order, and the slope of temperature change that reflects the rate of temperature change of the device is calculated by linear fitting.

[0077] Based on the slope of temperature change, real-time temperature value, and historical maximum temperature, a high-temperature trend factor is determined; the high-temperature trend factor is used to characterize stray light interference caused by internal thermal radiation of the equipment.

[0078] Among them, the real-time temperature value refers to the temperature value (denoted as C) of the infrared imager under the current operating state, which is collected in real time by the temperature sensor module. It reflects the instantaneous data of the current temperature level of the equipment and is used to determine whether the equipment is in a high-temperature state.

[0079] The historical maximum temperature refers to the highest recorded temperature of the infrared imager during its past operation (denoted as ). (This is used to measure whether the current temperature of the device is at a high temperature).

[0080] The high-temperature trend factor is a parameter (denoted as g) calculated by combining the slope of temperature change, real-time temperature value, and historical maximum temperature. Its value is positively correlated with the equipment temperature level and the rate of temperature change; the higher the real-time temperature and the steeper the slope of temperature change, the larger the high-temperature trend factor.

[0081] For example, the high temperature tendency factor It can be calculated using the following formula:

[0082] ;

[0083] For example, if the real-time temperature value of the infrared imager is higher and the temperature slope increases more steeply in the short term, it reflects a stronger high-temperature performance of the current infrared imaging equipment, which initially indicates stronger stray light interference during imaging. In the calculation, the denominator ( We need to add a very small positive number to ensure that the denominator is not 0. For example, the minimum value can be taken as the empirical value of 0.01.

[0084] Stray light interference caused by internal thermal radiation of equipment refers to the interference phenomenon in which the core components of an infrared imager (such as the lens barrel, detector Dewar flask, and electronic circuit board) generate heat due to energy loss during operation, causing the equipment itself to radiate infrared rays outward, which in turn aggravates the light scattering of optical elements, allowing stray light to reach the detector image plane and ultimately affecting the quality of infrared imaging.

[0085] Based on time-series light intensity data, the standard deviation of light intensity signal fluctuations is determined.

[0086] The standard deviation of light intensity signal fluctuation (denoted as σ) is a statistical parameter calculated based on time-series light intensity data. It is used to quantify the degree of fluctuation of light intensity signal within a specific time period. The larger the standard deviation value, the more unstable and volatile the light intensity changes within that period; the smaller the value, the more gradual the light intensity changes.

[0087] It should be noted that, in the embodiments of the present invention, based on the dynamic data of ambient light intensity (light intensity time series data) collected in chronological order, the standard deviation of fluctuation that can quantify the intensity of light intensity fluctuation is obtained through statistical calculation.

[0088] The light intensity imaging dominance is determined based on the standard deviation of the light intensity signal fluctuation, the real-time light intensity value, and the historical light intensity mean. The light intensity imaging dominance is used to characterize external stray light interference caused by ambient light.

[0089] Among them, the real-time light intensity value refers to the light intensity value of the current environment near the infrared imager (denoted as P) collected in real time by the light intensity sensing module. It is instantaneous data reflecting the current ambient light intensity level and directly reflects the intensity of the current ambient light.

[0090] Historical light intensity mean refers to the average value (denoted as P') of all ambient light intensity data collected and stored by the infrared imager during its past operation, used to measure whether the current ambient light intensity is at a normal level.

[0091] Light intensity imaging superiority refers to a parameter (denoted as f) calculated by combining the standard deviation of light intensity signal fluctuation, real-time light intensity value, and historical average light intensity. Its value is negatively correlated with real-time light intensity value and standard deviation of light intensity fluctuation. The smaller the real-time light intensity and the smoother the light intensity fluctuation, the higher the light intensity imaging superiority, and vice versa.

[0092] It should be noted that the above analysis only considers the impact of high equipment temperature on stray light interference. In actual scenarios, stray light interference in imaging is also affected by the combined interference of light intensity. Therefore, in order to improve the accuracy of the analysis of stray light interference, it is necessary to further analyze the stray light interference response of light intensity. Specifically, strong light sources such as sunlight have extremely high infrared radiation energy. Even after attenuation by the optical system (such as light shields and filters), a large amount of stray light still directly reaches the infrared detector. This stray light will be scattered on the inner wall of the lens barrel, thereby introducing strong light outside the field of view into the image plane, thus masking the infrared signal of the target. Moreover, when the light intensity fluctuates drastically in a short period of time, the unstable light reflection will also exacerbate stray light interference.

[0093] For example, the light intensity time-series signal at the location of the infrared imager used is obtained within a short period of 3 seconds, and the standard deviation of the light intensity signal fluctuation during this period is calculated. and the standard deviation of the fluctuation After normalization, we get Calculate the historical average light intensity during the operation of the infrared imager. .

[0094] Let the light intensity at the location of the real-time infrared imager be denoted as . The light intensity imaging dominance near the current infrared imager It can be calculated using the following formula:

[0095] ;

[0096] In the formula, if the light intensity value near the current infrared imager is smaller and the light intensity fluctuation is weaker, it indicates that the light intensity near the infrared imager is better, which further indicates that the real-time light intensity is less likely to interfere with the imaging stray light.

[0097] It should be noted that in actual imaging scenarios, the probability of the maximum ambient light intensity being 0 during the historical operation of an infrared imager is too low to be considered.

[0098] External stray light interference caused by ambient light refers to the phenomenon where infrared radiation generated by external ambient light sources such as sunlight, after being attenuated by the optical system, still has some stray light that directly reaches the infrared detector, or is introduced into the image plane after being scattered by the inner wall of the lens tube, causing the target infrared signal to be masked, thus affecting the infrared imaging quality.

[0099] The influence of stray light on imaging is determined based on the high temperature tendency factor and the light intensity imaging priority.

[0100] Among them, the stray light imaging influence refers to the parameter (denoted as F) calculated by combining the high temperature trend factor and the light intensity imaging dominance. Its value is positively correlated with the high temperature trend factor and negatively correlated with the light intensity imaging dominance. The larger the high temperature trend factor and the lower the light intensity imaging dominance, the higher the influence. It is used to quantify the comprehensive degree of basic stray light interference on infrared imaging under the combined effect of high temperature and ambient light of the equipment.

[0101] For example, if the current infrared imager has a real-time high temperature trend factor The larger the value, the better the imaging dominance of the light intensity near the imager. The lower the value, the greater the likelihood that the current infrared imager will be affected by strong stray light interference due to poor equipment quality, high temperature, and light intensity. This indicates the increased impact of stray light on the current infrared imager's imaging performance. It can be calculated using the following formula:

[0102] ;

[0103] The norm() function is a maximum and minimum value normalization function. To optimize light intensity imaging. It is a high-temperature trend factor.

[0104] Step S3: Correct the stray light imaging influence based on dust particle concentration data to obtain the strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the comprehensive probability of strong stray light interference in infrared images.

[0105] Among them, the dust particle concentration data refers to the real-time concentration value (denoted as A) of suspended dust particles (e.g., particles with a particle size similar to the infrared wavelength) in the environment near the infrared imager, which is collected by the particulate matter concentration sensing module, as well as the average dust particle concentration recorded during the past operation of the infrared imager (denoted as A'). It is a key data reflecting the influence of dust particles on stray light scattering in the imaging environment.

[0106] The strong stray light tendency coefficient (IF) is a parameter obtained by correcting for stray light imaging influence based on stray light impact, combined with dust particle concentration data (the difference between real-time dust particle concentration and historical average dust particle concentration) using a specific formula (e.g., processing with the hyperbolic tangent function th()). Its value is positively correlated with stray light imaging influence and real-time dust particle concentration; the higher the stray light imaging influence and the higher the real-time dust particle concentration compared to the historical average, the larger the coefficient.

[0107] The overall probability of strong stray light interference refers to the likelihood that strong stray light interference will occur and affect the image quality during the infrared imaging process due to the combined effects of multiple factors such as internal thermal radiation of the equipment, external ambient light, and dust particle scattering. It is a quantitative representation of the overall risk of stray light interference.

[0108] Furthermore, the step of correcting the stray light imaging influence based on dust particle concentration data to obtain a strong stray light tendency coefficient includes:

[0109] Obtain the historical average dust particle concentration of the infrared imager.

[0110] Infrared imagers are devices that receive infrared radiation emitted by objects, convert it into electrical signals, and process it to generate thermal images. They can be used for non-contact measurement and thermal distribution analysis. However, the imaging process may be affected by stray light, dust particles, and other factors.

[0111] Historical dust particle concentration refers to the set of concentration data of suspended dust particles (especially particles with a diameter similar to the infrared wavelength) in the imaging environment collected and stored by the particulate matter concentration sensing module during the past operation of the infrared imager, which records the dust particle distribution in the imaging environment at different times.

[0112] It should be noted that, in the embodiments of the present invention, the average value (denoted as A') obtained by summing all historical dust particle concentration data of the infrared imager and dividing by the total number of data is used to measure the overall level of dust particle concentration in the imaging environment during historical periods.

[0113] The correction factor is determined based on real-time dust particle concentration data and historical average dust particle concentration.

[0114] Among them, real-time dust particle concentration data refers to the concentration value (denoted as A) of suspended dust particles (especially particles with a particle size similar to the infrared wavelength) in the current imaging environment of the infrared imager, which is collected in real time by the particulate matter concentration sensing module. It is an instantaneous data reflecting the degree of dust particle pollution in the current imaging environment and directly reflects the potential impact of current dust particles on stray light scattering.

[0115] For example, the correction factor can be determined using the hyperbolic tangent function (th()), a commonly used nonlinear mathematical function whose value ranges between [-1, 1] and has the characteristic of smooth transition. In the embodiments of the present invention, it is used to normalize the difference between real-time dust particle concentration data and historical average dust particle concentration, mapping the degree of deviation in dust particle concentration to a reasonable range, thereby generating a stable correction factor.

[0116] The correction factor is a parameter calculated using the hyperbolic tangent function based on the difference between the real-time dust particle concentration and the historical average dust particle concentration. It is used to quantify the degree of deviation of the current dust particle concentration from the historical normal level and serves as an intermediate parameter for subsequent correction of stray light imaging influence and calculation of strong stray light tendency coefficient.

[0117] The influence of stray light on imaging is corrected based on the correction factor to obtain the strong stray light tendency coefficient.

[0118] It should be noted that, in the embodiments of the present invention, the influence of stray light imaging is optimized and adjusted according to the correction factor, and finally a strong stray light tendency coefficient is obtained, which can comprehensively characterize the probability of strong stray light interference under the superposition of multiple factors, providing data basis for subsequent judgment of stray light suppression requirements and selection of imaging control mode.

[0119] For example, suppose we acquire real-time dust particle concentration data of the imaging environment of an infrared imager. Simultaneously, the historical average dust particle concentration of the imaging environment during the historical operation of the infrared imager was calculated. .

[0120] Current infrared imager's real-time strong stray light tendency coefficient The following formula can be used for calculation:

[0121] ;

[0122] In the formula, if the dust particle concentration level in the current infrared imaging environment is higher than the dust particle concentration level in the overall historical imaging, it reflects that the imaging light path is more likely to experience increased stray light scattering. If the influence of real-time stray light imaging is also higher, it further reflects that the probability of real-time infrared imaging being interfered with by strong stray light is higher.

[0123] Step S4: Determine the overall anti-stray light stability based on the texture difference degree of the image data and the radiometric stability value corresponding to the infrared image; the texture difference degree is used to characterize the degree of structural consistency difference between the infrared image and the original RGB image.

[0124] Among them, image data refers to two types of image sets acquired by the infrared imager, including real-time short-term RGB original images of each frame, and infrared images in low stray light mode and high resolution mode (including multiple frames of comparison images within 1 second and real-time main analysis images).

[0125] It should be noted that the imaging time of a single infrared image is generally tens of milliseconds. Therefore, several infrared images and RGB original images of the target object are captured over a period of 1 second. The last adjacent infrared image is used as the real-time main analysis image, and the remaining images are used as control analysis group data for auxiliary analysis.

[0126] It should be noted that spatial registration and resolution resampling are performed on the acquired RGB original image and infrared image to establish a mapping relationship of pixel coordinates. This ensures that the same coordinates of the two images correspond to the same physical scene point during subsequent analysis. The low stray light mode focuses on strong background suppression and temporal smoothing and noise reduction, while the high resolution mode focuses on detail and texture enhancement and sharpening.

[0127] For example, the texture difference degree U can be obtained by extracting the LBP texture values ​​of the same SIFT corner points in the infrared image and the original RGB image at the same time, converting the two LBP texture values ​​into equal-length binary vectors, and then comparing and counting the number of bits with different values ​​at corresponding bits (i.e., the Hamming distance of the texture features). This is used to quantify the degree of difference between the infrared image and the original RGB image in terms of structural consistency. The greater the difference degree, the higher the possibility that the point is affected by stray light interference.

[0128] For example, taking any SIFT corner point in low stray light mode real-time infrared imaging as an example, the LBP texture value of that corner point is calculated. Simultaneously calculate the LBP texture value of the RGB image at that corner point at the same instant. After converting both into equal-length binary vectors, the texture difference of the corner point is determined by statistically analyzing the number of bits with different values ​​(i.e., the Hamming distance of the texture features). This value represents the difference in structural consistency between the RGB image and the infrared image. The larger the value, the greater the possibility that the corner point is affected by strong stray light interference during imaging. Therefore, the texture difference of each corner point in the infrared imaging needs to be calculated and recorded.

[0129] The radiometric stability value corresponding to an infrared image refers to the value obtained by analyzing multiple frames of infrared images within 1 second under the same control mode. One frame is used as the real-time master infrared image, and the remaining frames are used as control images. The absolute value of the difference between the radiometric response values ​​of each corner point in the real-time infrared image and the same point in the control image is calculated. The mean value is first calculated and then normalized to obtain the variation parameter (Q). This value is then transformed to obtain a numerical value characterizing the radiometric stability of the infrared image. The smaller this value, the smoother the change in the radiometric response value of the infrared image in the short term, and the stronger the resistance to fluctuations in the radiometric response value caused by stray light.

[0130] The overall anti-stray light stability is used to characterize the overall ability of infrared images to resist stray light interference. It refers to the parameter calculated by combining texture difference degree and radiation stability value. It is used to comprehensively quantify the structural consistency and radiation stability of infrared images under stray light interference. The higher the value, the better the overall performance of the image in resisting stray light interference.

[0131] RGB original images refer to images generated by the reflection of visible light from the surface of an object. They can reflect the real structure and texture details of a scene and serve as a benchmark image for comparing and analyzing stray light interference with infrared images.

[0132] The degree of structural consistency difference is used to measure the degree of similarity between the infrared image and the original RGB image in terms of texture, contour and other structural features at the same physical scene point. Stray light interference will destroy this consistency and lead to an increase in the degree of difference. Texture difference is a quantitative representation of this degree of difference.

[0133] It should be noted that, in the embodiments of the present invention, the determination process of the overall anti-stray light stability is based on the original RGB image acquired by the infrared imager and the infrared images in two modes. First, the texture difference between the infrared image and the original RGB image is calculated (quantifying the difference in structural consistency between the two). Then, the radiation stability value of the infrared image itself is combined (quantifying the radiation fluctuation in the short term). By comprehensively analyzing the two, the overall anti-stray light stability, which characterizes the overall anti-stray light performance of the infrared image, is finally obtained. The core role of the texture difference is to reflect the difference in structural consistency between the infrared image and the reference RGB image, providing a structural basis for the anti-stray light stability analysis.

[0134] Furthermore, the infrared image includes a first infrared image corresponding to a low stray light mode and a second infrared image corresponding to a high-resolution mode. The determination of overall stray light resistance stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared image includes:

[0135] In the low stray light mode, the first texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original RGB image and the first infrared image.

[0136] The low stray light mode refers to a working mode of infrared imagers that focuses on strong background suppression and temporal smoothing noise reduction. By sacrificing some image detail resolution, it reduces the impact of stray light interference on image quality and improves the image signal-to-noise ratio. It is suitable for scenarios with strong stray light interference.

[0137] The first infrared image refers to the real-time infrared image acquired by the infrared imager in low stray light mode, including the real-time main analysis image and the same mode infrared image in the control analysis group within 1 second.

[0138] Corner points refer to the scale-invariant feature transform (SIFT) feature points extracted from the original RGB image and the first infrared image. These points have stable texture features and serve as reference points for comparing corresponding positions in the two images.

[0139] Texture value refers to the local binary pattern (LBP) texture feature value extracted from corresponding corner points in the original RGB image and the first infrared image, which is used to quantify the texture structure information of that point.

[0140] Hamming distance of texture features is used to characterize the difference in texture structure between corner points of the same physical scene between the first infrared image and the original RGB image. It refers to converting the texture features (e.g., LBP texture values) of corresponding corner points of the two images into equal-length binary vectors, and then comparing and counting the number of bits with different values. The magnitude of the value directly reflects the consistency of the texture structure of the two types of images. The smaller the value, the more similar the texture and the weaker the stray light interference. The larger the value, the more significant the texture difference and the stronger the stray light interference.

[0141] The first texture difference refers to the parameter determined by the Hamming distance of the texture features of corresponding corner points of the original RGB image and the first infrared image under low stray light mode. It is used to quantify the degree of difference in structural consistency between the infrared image and the reference RGB image under low stray light mode. The larger the difference, the higher the difference, indicating that the corresponding point is more likely to be affected by stray light interference under low stray light mode.

[0142] Based on the radiation response values ​​of the same corner points of the first infrared image in the control analysis group, a first radiation stability value is determined; the first radiation stability value is used to characterize the radiation intensity stability of the first infrared image in a short time series.

[0143] The control analysis group refers to the set of remaining images other than the real-time main analysis image among several infrared images taken by the infrared imager for 1 second in low stray light mode.

[0144] The same corner point refers to the corner point (feature point) that is spatially identical, determined by the SIFT feature extraction algorithm from different images (such as the real-time master analysis image of an infrared imager in the same working mode and other images of the same mode in the control analysis group).

[0145] The radiation response value refers to the quantized electrical signal value (such as voltage, grayscale value, etc.) that the infrared radiation energy (generated by the thermal radiation of an object) at a corner point of the same physical scene is converted into when the detector of an infrared imager captures the infrared radiation at that corner point. This value is used to reflect the infrared radiation intensity at the corner point.

[0146] The first radiation stability value refers to the parameter obtained by calculating the absolute value of the difference between the radiation response value of each corner point in the first infrared image (real-time main analysis image) and the radiation response value of the same corner point in the control analysis group, and normalizing it after averaging. It is used to quantify the stability of the radiation intensity of the infrared image under the corresponding control mode in the short-term time series.

[0147] Short-term time series refers to a continuous time interval of 1 second (based on the characteristic that a single frame of an infrared imager only requires tens of milliseconds to capture, multiple frames of images can be acquired within 1 second). During this period, the change in the thermal radiation of the target object itself can be ignored, and only the fluctuation in radiation intensity caused by stray light interference needs to be considered.

[0148] Radiation intensity stability refers to the stability of the infrared radiation intensity corresponding to the same corner point in the first infrared image within a short time series. The higher the stability, the smaller the impact of stray light interference on the infrared radiation signal.

[0149] It should be noted that the determination of the first radiation stability value under low stray light mode is based on the first infrared image under low stray light mode and other infrared images of the same mode in the control analysis group. The radiation response value (the quantized electrical signal value converted after the infrared detector captures the infrared radiation at that point) of the same corner point in each image is extracted, and the difference is calculated and the mean is normalized.

[0150] A first comprehensive anti-stray light stability is determined based on a first texture difference degree and a first radiation stability value; the first comprehensive anti-stray light stability is used to characterize the comprehensive ability of a first infrared image to resist stray light interference in a low stray light mode.

[0151] Among them, the first comprehensive anti-stray light stability is a combination of the first texture difference and the first radiation stability value. The parameters are calculated using the following formula:

[0152] ;

[0153] For example, let the first texture difference be... It is also necessary to... Perform normalization on the same scale (e.g., divide by 255) to obtain The first comprehensive anti-stray light stability is a comprehensive quantitative index of the anti-stray light performance of infrared images in low stray light mode. The higher the value, the stronger the comprehensive ability to resist stray light interference. The denominator in the calculation... and denominator A very small positive number needs to be added to ensure that the denominator is not 0. For example, the minimum value can be taken as the empirical value of 0.01.

[0154] Furthermore, the determination of comprehensive anti-stray light stability based on the texture difference of image data and the radiometric stability value corresponding to the infrared image includes:

[0155] In high-resolution mode, the second texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original RGB image and the second infrared image.

[0156] The second infrared image refers to the infrared image acquired by the infrared imager in high-resolution mode, including the real-time main analysis image and the same mode infrared image in the control analysis group within 1 second.

[0157] The second texture difference refers to the parameter determined by the Hamming distance of the texture features of corresponding corner points in the original RGB image and the second infrared image under high resolution mode. It is used to quantify the degree of difference in structural consistency between the infrared image and the reference RGB image under high resolution mode. The larger the difference, the higher the difference, indicating that the corresponding point is more likely to be affected by stray light interference under high resolution mode.

[0158] The second radiation stability value is determined based on the radiation response values ​​of the same corner points of the second infrared images in the control analysis group; the second radiation stability value is used to characterize the radiation intensity stability of the second infrared images in a short time series.

[0159] The second radiation stability value refers to the parameter obtained by calculating the absolute value of the difference between the radiation response value of each corner point in the second infrared image (real-time main analysis image) and the radiation response value of the same corner point in the control analysis group, and normalizing it after averaging. It is used to quantify the stability of the radiation intensity of the infrared image under the corresponding control mode in the short-term time series.

[0160] The second comprehensive anti-stray light stability is determined based on the second texture difference degree and the second radiation stability value; the second comprehensive anti-stray light stability is used to characterize the comprehensive ability of the second infrared image to resist stray light interference in high resolution mode.

[0161] It should be noted that the calculation method for the second comprehensive anti-stray light stability is the same as that for the first comprehensive anti-stray light stability, and will not be repeated here.

[0162] Step S5: Determine the urgency of stray light suppression based on the comprehensive anti-stray light stability and strong stray light tendency coefficient.

[0163] Among them, the urgency of stray light suppression refers to a parameter calculated by combining the comprehensive stray light resistance stability (including the stability difference between the two modes) and the strong stray light tendency coefficient, through normalization and other specific algorithms. This parameter is used to quantify the urgency of suppressing stray light during infrared imaging. The higher the value, the more severe the real-time stray light interference, and the stronger the need to switch to a low stray light mode to suppress stray light.

[0164] Furthermore, determining the urgency of stray light suppression based on the comprehensive stray light stability and strong stray light tendency coefficient includes:

[0165] Based on the absolute value of the difference between the first comprehensive anti-stray light stability and the second comprehensive anti-stray light stability.

[0166] The absolute value of the difference refers to the absolute value of the result obtained by subtracting the values ​​of the first comprehensive anti-stray light stability and the second comprehensive anti-stray light stability. This value is used to quantify the degree of difference in anti-stray light stability between the two modes. The larger the absolute value of the difference, the more significant the difference in anti-stray light performance between the two modes; the smaller the absolute value of the difference, the closer the anti-stray light performance of the two modes is.

[0167] The urgency of stray light suppression is determined based on the absolute value of the difference and the strong stray light tendency coefficient.

[0168] For example, suppose the absolute value of the difference is The strong stray light tendency coefficient is The urgency of stray light suppression can be calculated using the following formula. :

[0169] ;

[0170] For infrared imaging in real-time low stray light mode, the first comprehensive stray light stability of infrared imaging in real-time low stray light mode is obtained by averaging the stray light stability at each corner point. Similarly, the second comprehensive stray light stability of infrared imaging in real-time high resolution mode is calculated, and the absolute value of the difference between the comprehensive stray light stability of the two control modes is obtained. The larger this value, the greater the demand for stray light suppression in the real-time low stray light mode, and the stronger the real-time stray light tendency coefficient. The larger the value, the higher the requirement for real-time stray light suppression.

[0171] Step S6: Determine the target imaging control mode based on the urgency of stray light suppression and the preset threshold, so that the infrared imager can perform adaptive imaging control according to the target imaging control mode.

[0172] The preset threshold, denoted as T, is a baseline value pre-set to determine the required level of stray light suppression based on the application scenario, imaging accuracy requirements, and anti-stray light performance indicators of the infrared imager. This threshold has been determined through experimental verification and engineering calibration and can be dynamically adjusted according to the actual application scenario.

[0173] The target imaging control mode refers to the working mode to be executed by the infrared imager, which is determined by comparing and analyzing the urgency of stray light suppression with the preset threshold, so that the infrared imager can work in a low stray light mode or a high resolution mode.

[0174] Adaptive imaging control refers to an infrared imager automatically adjusting its internal imaging parameters (such as exposure time, gain, and filtering parameters) according to a determined target imaging control mode, enabling the imaging control system to adapt to the real-time stray light environment. Without manual intervention, it can dynamically adapt to prioritize suppressing stray light when interference is severe and prioritizing detail preservation when interference is minor, ensuring optimal image quality output under different conditions.

[0175] It should be noted that, in the embodiments of the present invention, the infrared imager alternately acquires two frames of images in different modes within a calibration cycle for calculation, and enters a stable operation phase after determining the target imaging control mode.

[0176] Furthermore, determining the target imaging control mode based on the urgency of stray light suppression and a preset threshold includes:

[0177] When the urgency of stray light suppression is greater than a preset threshold, the target imaging control mode is determined to be the low stray light mode.

[0178] It should be noted that when the value of the stray light suppression urgency level is greater than the preset benchmark judgment threshold, it is determined that the stray light interference has reached the level that needs to be suppressed first, and then the target imaging control mode of the infrared imager is determined to be the low stray light mode, so as to ensure the imaging stability in complex stray light environment.

[0179] When the urgency of stray light suppression is less than or equal to a preset threshold, the target imaging control mode is determined to be the high-resolution mode.

[0180] It should be noted that when the stray light suppression urgency value is less than or equal to the preset benchmark judgment threshold, it is determined that the stray light interference is relatively mild and there is no need to suppress stray light first. Therefore, the target imaging control mode of the infrared imager is set to high resolution mode to ensure the detail and clarity of the image.

[0181] For example, the preset threshold can be 0.75. This threshold is based on a large number of experimental calibrations and corresponds to the critical point at which stray light can be perceived by the human eye as significantly affecting the image quality. When the real-time stray light suppression urgency of the infrared imager is higher than the threshold of 0.75, it is considered that the infrared imaging process is greatly affected by stray light interference. At this time, the infrared imager is switched to a low stray light mode. Conversely, if the real-time stray light interference is considered to be small and the necessity of stray light intervention is low, then the high definition of infrared imaging is prioritized and the infrared imager is switched to a high resolution mode.

[0182] Furthermore, after determining the target imaging control mode based on the urgency of stray light suppression and a preset threshold, the method further includes:

[0183] For a target with known radiation intensity, the gain and bias coefficient of each pixel in the infrared image are determined, and the non-uniformity correction of the infrared image is performed by a two-point correction method.

[0184] Here, a target with known radiation intensity refers to a standard radiation source (such as a blackbody radiation source) whose radiation characteristics are stable and traceable, having been calibrated. It serves as a reference standard for infrared imager calibration, used to acquire standard data of the pixel response of infrared images.

[0185] In an infrared image, the gain of each pixel represents the amplification factor of a single pixel to the incident infrared radiation signal. It is used to correct the problem of inconsistent response amplitudes of different pixels to signals of the same radiation intensity. The gain value is calculated from the standard signal of a target with known radiation intensity and the actual output signal of the corresponding pixel, which can make the response sensitivity of each pixel tend to be consistent.

[0186] The bias coefficient refers to the reference value of the output signal of a single pixel in an infrared image when there is no infrared radiation incident (or the incident radiation intensity is zero). It is used to correct the dark current offset error of the pixel. Similar to gain, the bias coefficient is determined by calibration using a target with known radiation intensity to eliminate the difference in the reference signal caused by the pixel's own noise.

[0187] Two-point correction refers to a commonly used algorithm for non-uniformity correction of infrared images. It selects two standard targets with known radiation intensities (usually low-intensity targets and high-intensity targets), obtains the output signals of each pixel in the infrared image under these two standard radiations, calculates the gain and bias coefficient of each pixel, and finally completes the non-uniformity correction of the entire pixel through a preset formula.

[0188] Non-uniformity correction is used to eliminate fixed pattern noise (such as stripes and spots) in images caused by differences in the response characteristics of pixels in the detector array (such as inconsistent gain and bias offset). By applying calculated gain and bias coefficients to correct the output signal of each pixel, areas with the same radiation intensity in the image exhibit uniform grayscale / radiance, thereby improving image quality and imaging accuracy.

[0189] The bad pixels / blind pixels in the infrared image are marked and detected by the bad pixel map, and the bad pixels / blind pixels are replaced by directional interpolation to obtain the repaired infrared image.

[0190] Among them, the bad pixel map refers to the calibration map generated by a special testing process before the infrared imager leaves the factory or during periodic calibration. It records the location information of all bad pixels / blind pixels in the detector array and may include the coordinate data of bad pixels / blind pixels.

[0191] Bad pixels / blind pixels refer to pixels in the array that exhibit abnormal responses, all of which are imaging failures. Bad pixels are those that are overly sensitive to infrared radiation signals (i.e., output signal is abnormally high) or overly sluggish (i.e., output signal is abnormally low); blind pixels are those that have no response at all (i.e., output signal is constantly zero or a fixed value). Both can lead to bright spots, dark spots, and other noise in the image, compromising image integrity.

[0192] Marker detection refers to the process of comparing real-time acquired infrared images pixel by pixel with a bad pixel image as a reference. By matching the coordinate information in the bad pixel image, bad pixels / blind pixels in the infrared image are accurately located and marked.

[0193] Directional interpolation refers to an optimized interpolation algorithm for image defect repair. Unlike traditional uniform interpolation, this algorithm first analyzes the texture direction, gray-level gradient, and other features of normal pixels around the defective pixel / blind pixel. Then, it selects the gray-level of the surrounding effective pixels along a path consistent with the texture direction and performs weighted calculation to obtain a repair value that adapts to the surrounding image features.

[0194] Replacement processing refers to the process of replacing the abnormal output values ​​of marked bad pixels / blind pixels in an infrared image with the repair values ​​calculated using directional interpolation. This eliminates noise caused by bad pixels / blind pixels, restores normal pixel information in the image, and improves image integrity and clarity.

[0195] The restored infrared image refers to the infrared image obtained after defect / blind pixel marker detection and directional interpolation replacement. This image has eliminated most of the defect / blind pixel noise, has complete pixel information and continuous texture, and can be used as input data for subsequent processes such as non-uniformity correction, stray light interference analysis, and adaptive imaging control.

[0196] In summary, during the automatic imaging control process for stray light suppression, the embodiments of the present invention first acquire image data and corresponding stray light data; determine the stray light imaging influence degree based on equipment temperature time-series data and light intensity time-series data; correct the stray light imaging influence degree based on dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the comprehensive probability of strong stray light interference in the infrared image; determine the comprehensive stray light suppression stability based on the texture difference degree of the image data and the radiation stability value corresponding to the infrared image; determine the urgency of stray light suppression based on the comprehensive stray light suppression stability and the strong stray light tendency coefficient; and determine the target imaging control mode based on the urgency of stray light suppression and a preset threshold, so that the infrared imager can perform adaptive imaging control according to the target imaging control mode. This invention can automatically select the imaging control mode of an infrared imager based on the stray light interference behavior during infrared imaging. Therefore, it first analyzes the stray light tendency coefficient based on the stray light tendency behavior reflected by the high temperature of the equipment and the external environment, and then obtains the stray light suppression urgency parameter by combining the stray light effect feedback of real-time infrared imaging. Based on the real-time stray light condition performance reflected by the stray light suppression urgency parameter, the target imaging control mode of the infrared imager is determined, thereby improving the anti-stray light imaging control accuracy during infrared imaging.

[0197] This invention proposes an automatic imaging control system for infrared imaging that resists stray light. Please refer to [link / reference]. Figure 2 The diagram illustrates a structural schematic of an anti-stray light automatic imaging control system 200 for infrared imaging according to an embodiment of the present invention. The system includes:

[0198] The acquisition module 201 is used to acquire image data and stray light data corresponding to the image data; the stray light data includes: device temperature time series data, light intensity time series data, and dust particle concentration data; the image data includes RGB original image and infrared image.

[0199] The determination module 202 is used to determine the stray light imaging influence degree based on the equipment temperature time series data and light intensity time series data; the stray light imaging influence degree is used to characterize the basic stray light interference of the infrared image;

[0200] Module 203 is obtained to correct the influence of stray light imaging based on dust particle concentration data, and to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the overall probability of strong stray light interference in infrared images;

[0201] The determination module is also used to determine the overall anti-stray light stability based on the texture difference degree of the image data and the radiometric stability value corresponding to the infrared image; the texture difference degree is used to characterize the degree of structural consistency difference between the infrared image and the original RGB image; the urgency of stray light suppression is determined according to the overall anti-stray light stability and the strong stray light tendency coefficient; and the target imaging control mode is determined according to the urgency of stray light suppression and the preset threshold, so that the infrared imager can perform adaptive imaging control according to the target imaging control mode.

[0202] In one embodiment, a submodule is defined for determining the temperature change slope based on time-series temperature data of the device; determining a high-temperature trend factor based on the temperature change slope, real-time temperature value, and historical maximum temperature value; the high-temperature trend factor is used to characterize stray light interference caused by internal thermal radiation of the device; determining the standard deviation of light intensity fluctuation based on time-series light intensity data; determining the light intensity imaging dominance based on the standard deviation of light intensity fluctuation, real-time light intensity value, and historical average light intensity value; the light intensity imaging dominance is used to characterize external stray light interference caused by ambient light; and determining the stray light imaging influence degree based on the high-temperature trend factor and the light intensity imaging dominance degree.

[0203] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the anti-stray light automatic imaging control system for infrared imaging and the anti-stray light automatic imaging control method for infrared imaging provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0204] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0205] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0206] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0207] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0208] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0209] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0210] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0211] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0212] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the anti-stray light automatic imaging control method for infrared imaging provided in the above embodiments.

[0213] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0214] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for automatic imaging control against stray light in infrared imaging, characterized in that, The method includes: Acquire image data and stray light data corresponding to the image data; the stray light data includes: device temperature time-series data, light intensity time-series data, and dust particle concentration data; the image data includes red, green, and blue original images and infrared images; Based on the device temperature time-series data and the light intensity time-series data, the stray light imaging influence degree is determined; the stray light imaging influence degree is used to characterize the basic stray light interference of the infrared image; The influence of stray light on imaging is corrected based on the dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the overall probability of strong stray light interference in the infrared image; Based on the texture difference degree of the image data and the radiometric stability value corresponding to the infrared image, the overall anti-stray light stability is determined; the texture difference degree is used to characterize the degree of structural consistency difference between the infrared image and the original red, green and blue image. The urgency of stray light suppression is determined based on the overall anti-stray light stability and the strong stray light tendency coefficient. Based on the urgency of stray light suppression and the preset threshold, a target imaging control mode is determined so that the infrared imager can perform adaptive imaging control according to the target imaging control mode.

2. The automatic imaging control method for infrared imaging with anti-stray light according to claim 1, characterized in that, The step of determining the stray light imaging impact degree based on the device temperature time-series data and the light intensity time-series data includes: Based on the temperature time-series data of the device, determine the slope of temperature change; Based on the temperature change slope, real-time temperature value, and historical maximum temperature, a high-temperature trend factor is determined; the high-temperature trend factor is used to characterize stray light interference caused by internal thermal radiation of the equipment. Based on the aforementioned light intensity time series data, the standard deviation of the light intensity signal fluctuation is determined; The light intensity imaging superiority is determined based on the standard deviation of the light intensity signal fluctuation, the real-time light intensity value, and the historical light intensity mean; the light intensity imaging superiority is used to characterize external stray light interference caused by ambient light. Based on the high temperature tendency factor and the light intensity imaging preference, the influence of stray light imaging is determined.

3. The automatic imaging control method for infrared imaging with anti-stray light according to claim 1, characterized in that, The step of correcting the stray light imaging influence based on the dust particle concentration data to obtain a strong stray light tendency coefficient includes: Obtain the historical average dust particle concentration of the infrared imager; The correction factor is determined based on the real-time dust particle concentration data and the historical average dust particle concentration. The stray light imaging influence degree is corrected based on the correction factor to obtain the strong stray light tendency coefficient.

4. The automatic imaging control method for infrared imaging with anti-stray light according to claim 1, characterized in that, The infrared images include a first infrared image corresponding to a low stray light mode and a second infrared image corresponding to a high-resolution mode. The determination of overall stray light resistance stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared images includes: In the low stray light mode, the first texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original red-green-blue image and the first infrared image; Based on the radiation response values ​​of the same corner points of the first infrared images in the control analysis group, a first radiation stability value is determined; the first radiation stability value is used to characterize the radiation intensity stability of the first infrared images in a short time series. A first comprehensive anti-stray light stability is determined based on the first texture difference degree and the first radiation stability value; the first comprehensive anti-stray light stability is used to characterize the comprehensive ability of the first infrared image to resist stray light interference in the low stray light mode.

5. The automatic imaging control method for infrared imaging with anti-stray light according to claim 4, characterized in that, The determination of overall anti-stray light stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared image includes: In the high-resolution mode, the second texture difference is determined based on the Hamming distance of the texture features of the corner points corresponding to the original red-green-blue image and the second infrared image; Based on the radiation response values ​​of the same corner points of the second infrared image in the control analysis group, a second radiation stability value is determined; the second radiation stability value is used to characterize the radiation intensity stability of the second infrared image in a short time series. A second comprehensive anti-stray light stability is determined based on the second texture difference degree and the second radiation stability value; the second comprehensive anti-stray light stability is used to characterize the comprehensive ability of the second infrared image to resist stray light interference in the high-resolution mode.

6. The automatic imaging control method for infrared imaging with anti-stray light according to claim 5, characterized in that, The determination of the urgency of stray light suppression based on the comprehensive stray light stability and the strong stray light tendency coefficient includes: Based on the absolute value of the difference between the first comprehensive anti-stray light stability and the second comprehensive anti-stray light stability; The urgency of stray light suppression is determined based on the absolute value of the difference and the strong stray light tendency coefficient.

7. The automatic imaging control method for infrared imaging with anti-stray light according to claim 1, characterized in that, The step of determining the target imaging control mode based on the urgency of stray light suppression and a preset threshold includes: If the urgency of stray light suppression is greater than the preset threshold, the target imaging control mode is determined to be a low stray light mode; If the urgency of stray light suppression is less than or equal to the preset threshold, the target imaging control mode is determined to be a high-resolution mode.

8. The automatic imaging control method for infrared imaging with anti-stray light according to claim 1, characterized in that, After determining the target imaging control mode based on the stray light suppression urgency and the preset threshold, the method further includes: For a target with known radiation intensity, the gain and bias coefficient of each pixel in the infrared image are determined, and the non-uniformity correction of the infrared image is performed by a two-point correction method. The infrared image is marked and detected by using a bad pixel map, and the bad pixels / blind pixels are replaced by a directional interpolation method to obtain a repaired infrared image.

9. An anti-stray light automatic imaging control system for infrared imaging, characterized in that, The system includes: The acquisition module is used to acquire image data and stray light data corresponding to the image data; the stray light data includes: device temperature time-series data, light intensity time-series data, and dust particle concentration data; the image data includes red, green, and blue original images and infrared images. The determination module is used to determine the stray light imaging influence degree based on the device temperature time-series data and the light intensity time-series data; the stray light imaging influence degree is used to characterize the basic stray light interference of the infrared image; The module is used to correct the stray light imaging influence based on the dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the overall probability of strong stray light interference in the infrared image; The determining module is further configured to determine the overall anti-stray light stability based on the texture difference degree of the image data and the radiometric stability value corresponding to the infrared image; the texture difference degree is used to characterize the degree of structural consistency difference between the infrared image and the original red, green and blue images; determine the stray light suppression urgency according to the overall anti-stray light stability degree and the strong stray light tendency coefficient; and determine the target imaging control mode according to the stray light suppression urgency degree and a preset threshold, so that the infrared imager performs adaptive imaging control according to the target imaging control mode.

10. The anti-stray light automatic imaging control system for infrared imaging according to claim 9, characterized in that, The determining module includes: The determination submodule is used to: determine the temperature change slope based on the device temperature time-series data; determine a high-temperature trend factor based on the temperature change slope, real-time temperature value, and historical maximum temperature value; the high-temperature trend factor is used to characterize stray light interference caused by internal thermal radiation of the device; determine the light intensity fluctuation standard deviation based on the light intensity time-series data; determine the light intensity imaging dominance based on the light intensity fluctuation standard deviation, real-time light intensity value, and historical average light intensity value; the light intensity imaging dominance is used to characterize external stray light interference caused by ambient light; and determine the stray light imaging influence degree based on the high-temperature trend factor and the light intensity imaging dominance degree.

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