WO2022041356A1 - Method, apparatus and device for correcting infrared image, and refrigeration infrared imaging system - Google Patents
Method, apparatus and device for correcting infrared image, and refrigeration infrared imaging system Download PDFInfo
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Definitions
- the present application relates to the technical field of infrared imaging, and in particular, to an infrared image correction method, device, equipment and refrigeration infrared imaging system.
- Refrigerated infrared imaging system is widely used in security, agriculture, industry and other fields due to its advantages of long detection distance, high sensitivity and strong detection ability.
- the refrigerated infrared detector is in a low temperature cavity when working, and the temperature in the low temperature cavity is generally between 90K and 110K, and the working environment temperature of the entire infrared imaging system is usually around 300K, so the infrared radiation received by the refrigerated infrared detector is not only Including the observation target, but also the internal components of the infrared imaging system, such as the lens barrel, lens, etc., appear on the image as bright rings and cold spots, that is, the cold reflection effect.
- the cold reflection effect is common in the refrigeration infrared imaging system, which seriously affects the detection and recognition ability of the infrared imaging system and the user's perception experience.
- the non-uniformity correction algorithm is generally suppressed or used from the optical aspect.
- Optical suppression requires a lot of work in optical design and coating, which is difficult, costly, and difficult to engineer.
- the non-uniformity correction algorithm cannot With full correction of cold reflections, speckles will still appear in the image.
- the purpose of the present application is to provide an infrared image correction method, device, equipment and refrigeration infrared imaging system, so as to improve the quality of the infrared image, which is simple and easy to implement.
- the present application provides a method for calibrating an infrared image, including:
- the picture state includes a static state and a motion state
- the to-be-corrected image is corrected by using the multi-frame images to obtain a corrected image.
- using the multi-frame images to correct the image to be corrected, and obtaining the corrected image includes:
- the picture state is the static state, selecting the multi-frame image from the multi-frame images that is closest to the collection environment information of the to-be-corrected image as a compensation matrix image;
- weighting processing is performed on the multi-frame images to obtain a template cold image; scene suppression processing is performed on the to-be-corrected image to obtain an estimated cold image; according to the template cold image and the estimated cold image, to determine the compensation matrix image;
- performing scene suppression processing on the to-be-corrected image to obtain an estimated cold image includes:
- the low-frequency information of the scene suppression is input into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
- the determining the confidence level of each absolute value of the difference in the absolute value matrix includes:
- the absolute value of the difference is not less than the first preset threshold, determine whether the absolute value of the difference is greater than a second preset threshold; the second preset threshold is greater than the first preset threshold;
- the confidence level of the absolute value of the difference is determined to be the second confidence level; the first confidence level is greater than the second confidence level;
- the confidence level of the absolute value of the difference is determined according to a preset function.
- determining the compensation matrix image according to the template cold image and the estimated cold image includes:
- the correlation coefficient is not greater than the coefficient threshold, determine the corresponding corresponding pixel in the estimated cold image, and take the pixel mean value of all the pixels in the preset neighborhood of the corresponding pixel as the compensation value;
- the compensation values constitute the compensation matrix image.
- performing weighting processing on the multi-frame images to obtain a cold template image includes:
- the judging the picture state of the image to be corrected includes:
- the frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
- the present application also provides an infrared image correction device, comprising:
- a first acquisition module configured to acquire multiple frames of images collected by the cooling infrared imaging system under preset influencing factors, and the multiple frames of images have cold reflection phenomenon;
- a second acquisition module configured to acquire the to-be-corrected image collected by the refrigeration infrared imaging system
- a judgment module for judging the picture state of the image to be corrected, where the picture state includes a static state and a motion state;
- a correction module configured to correct the image to be corrected by using the multi-frame images according to the picture state to obtain a corrected image.
- the application also provides an infrared image correction device, including:
- the processor is configured to implement the steps of any one of the above-mentioned infrared image correction methods when executing the computer program.
- the present application further provides a refrigerated infrared imaging system, the refrigerated infrared imaging system includes the above-mentioned infrared image correction device.
- a method for calibrating an infrared image includes acquiring multiple frames of images with cold reflections collected by a cooling infrared imaging system; obtaining an image to be corrected collected by the cooling infrared imaging system; The picture state; the picture state includes a static state and a motion state; according to the picture state, the image to be corrected is corrected by using the multi-frame images to obtain a corrected image.
- the infrared image correction method in the present application determines whether the picture state of the to-be-corrected image is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different picture states, use It is very simple and easy to correct the image to be corrected for the multi-frame images with cold reflection phenomenon, eliminate the cold reflection phenomenon in the to-be-corrected image, improve the image quality, and do not need to improve the optical design of the cooling infrared imaging system.
- the present application also provides a correction device, equipment and refrigeration infrared imaging system with the above advantages.
- FIG. 1 is a flowchart of a method for calibrating an infrared image provided by an embodiment of the present application
- 2 is a schematic diagram of simulating the radiation temperature of a target object and collecting multiple frames of images
- FIG. 3 is a flowchart of obtaining an estimated cold image provided by an embodiment of the present application.
- FIG. 4 is a flowchart of determining a compensation matrix image provided by an embodiment of the present application.
- FIG. 5 is a structural block diagram of an infrared image correction apparatus provided by an embodiment of the present application.
- FIG. 6 is a schematic structural diagram of a cooling infrared imaging system provided by an embodiment of the present application.
- the non-uniformity correction algorithm is generally suppressed or used from the optical aspect.
- Optical suppression requires a lot of work on optical design and coating, which is difficult, expensive, and difficult to engineer; the non-uniformity correction algorithm Cold reflections are not fully corrected, and speckles will still appear in the image.
- FIG. 1 is a flowchart of a method for calibrating an infrared image provided by an embodiment of the present application, and the method includes:
- Step S101 acquiring multiple frames of images with cold reflection phenomenon collected by a cooling infrared imaging system.
- the ambient temperature can be taken from -25°C to 35°C
- the radiation temperature of the target object is simulated by the black body temperature, see Figure 2 for details.
- the multi-frame images with the cold reflection phenomenon may be pre-collected and saved, or may be collected in real time, which is not specifically limited in this application.
- Step S102 acquiring the to-be-corrected image collected by the cooling infrared imaging system.
- the images to be corrected are consecutive frame images.
- Step S103 Determine the picture state of the image to be corrected; the picture state includes a static state and a motion state.
- the judging the picture state of the image to be corrected includes:
- the frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
- the frame difference method when the frame difference method is adopted, a difference operation is performed on the images to be corrected in two adjacent frames, and the pixels corresponding to different frames are subtracted to obtain the absolute value of the grayscale difference.
- the preset threshold that is, It can be judged that the picture state of the image to be corrected is a moving state, otherwise it is a static state.
- positioning methods can also be used, such as the aircraft positioning system GPS to determine whether the position has moved.
- a motion sensor such as a gyroscope, may also be used to determine whether the position has moved, and then determine whether the screen state is a static state or a moving state.
- Step S104 According to the picture state, correct the image to be corrected by using the multi-frame images to obtain a corrected image.
- using the multi-frame images to correct the image to be corrected, and obtaining the corrected image includes:
- Step S1041 when the picture state is the static state, select the multi-frame image that is closest to the collection environment information of the to-be-corrected image from the multi-frame images as a compensation matrix image.
- Collecting environmental information refers to the influencing factors in the process of collecting multiple frames of images with cold reflection, that is, the environmental temperature, the radiation temperature of the target object, and the focal length.
- Step S1042 when the picture state is the motion state, perform weighting processing on the multi-frame images to obtain a template cold image; perform scene suppression processing on the to-be-corrected image to obtain an estimated cold image; according to the template The cold image and the estimated cold image determine the compensation matrix image.
- performing weighting processing on the multi-frame images to obtain a cold template image includes:
- F 0 is the cold image of the template
- F a is the a-th frame image in the multi-frame image
- ka is the weight coefficient of the a-th frame image.
- Step S1043 Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.
- the corrected image is obtained by subtracting the pixel value corresponding to the compensation matrix image from the pixel value of the image to be corrected.
- the infrared image correction method in the present application determines whether the image state of the image to be corrected is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different
- the multi-frame image of the reflection phenomenon is used to correct the image to be corrected, eliminating the cold reflection phenomenon in the image to be corrected, improving the image quality, and without improving the optical design of the cooling infrared imaging system, which is very simple and easy to implement.
- the scene suppression processing on the to-be-corrected image to obtain an estimated cold image includes:
- Step S201 Determine a background pixel value according to the to-be-corrected image.
- the statistical histogram is used to perform statistics on all pixel values of the image to be corrected, and the corresponding pixel response value with the largest number of statistics is found in the histogram as the background pixel value I b suppressed by the overall scene information.
- the number of occurrences in the to-be-corrected image, and the schematic diagram of the statistical histogram is shown in Figure 4.
- Step S202 Calculate the absolute value of the difference between the pixel value of each pixel point in the image to be corrected and the background pixel value to obtain an absolute value matrix.
- I b is the background pixel value
- I is the pixel value of each pixel in the image to be corrected
- D(n) is the absolute value matrix
- Step S203 Determine the confidence level of each absolute value of the difference in the absolute value matrix, and obtain a confidence level matrix for suppressing the overall scene information.
- the determining the confidence level of each absolute value of the difference in the absolute value matrix includes:
- Step S2031 Determine whether the absolute value of the difference is less than a first preset threshold.
- the first preset threshold is not specifically limited in this application, and it depends on the situation.
- the first preset threshold may be 100, or 120, and so on.
- Step S2032 If the absolute value of the difference is smaller than a first preset threshold, determine the confidence level of the absolute value of the difference as a first confidence level.
- the first confidence level is not specifically limited in this application, and can be set by yourself.
- the first confidence level may be 0.9, or 0.85, and so on.
- Step S2033 If the absolute value of the difference is not less than a first preset threshold, determine whether the absolute value of the difference is greater than a second preset threshold; the second preset threshold is greater than the first preset threshold.
- the second preset threshold is not specifically limited in this application, and it is sufficient to ensure that the second preset threshold is greater than the first preset threshold.
- the second preset threshold may be 600, or 700, and so on.
- Step S2034 if the absolute value of the difference is greater than the second preset threshold, determine the confidence of the absolute value of the difference as a second confidence; the first confidence is greater than the second confidence Spend.
- the second confidence level is not specifically limited in this application, and it is sufficient to ensure that the second confidence level is smaller than the first confidence level.
- the second confidence level may be 0.2, or 0.15, and so on.
- Step S2035 If the absolute value of the difference is not greater than the second preset threshold, determine the confidence level of the absolute value of the difference according to a preset function.
- p(i,j) is the confidence level of the absolute value of the difference in the absolute value matrix whose coordinates are (i,j), pmax is the first confidence level, pmin is the second confidence level, and Thrmax is the first confidence level.
- Thr min is the first preset threshold
- D(i, j) is the absolute value of the difference with the coordinates (i, j) in the absolute value matrix.
- a point with a larger absolute value of the difference in the absolute value matrix indicates a higher probability that the point is a too dark or too bright target, the greater the interference of the point to the estimated cold image, and the lower the confidence.
- the point where the absolute value of the difference exceeds the second preset threshold is set to a smaller second confidence level.
- the gray level of the scene information has certain fluctuations, so the absolute value of the difference is set as the second confidence level.
- Points smaller than the smaller first preset threshold are set as larger first confidence levels.
- Step S204 Determine low-frequency information of the image to be corrected and gradient information of each pixel in the image to be corrected.
- the calculation method of the low-frequency information I low may be, but not limited to, the K*K mean filtering method, where K may be 3.
- the gradient information calculation formula is as formula (5),
- G(i,j) is the gradient information of the (i,j) pixel
- I(i,j) is the pixel value of the (i,j) pixel
- I(i+1,j) is the (i) +1,j)
- I(i,j+1) is the pixel value of the (i,j+1) pixel point.
- the purpose of calculating the gradient information is to avoid the interference of local edge information on the estimated cold image.
- Step S205 Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information.
- the low-frequency component calculation formula is as in Equation (6),
- Low is the low-frequency component
- I low is the low-frequency information
- G is the gradient information
- the function of the low frequency component is to strengthen the suppression of the local edge and texture information of the image to be corrected.
- Step S206 Determine scene suppression low-frequency information according to the confidence matrix and the low-frequency component.
- X is the scene suppression low-frequency information
- Low is the low-frequency component
- p is the confidence matrix
- Step S207 Inputting the low-frequency information of the scene suppression into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
- time-domain low-pass filtering algorithm formula is as shown in Equation (8),
- n is the number of multi-frame images with cold reflection phenomenon
- X(n) is the scene suppression low-frequency information of the n-th multi-frame image
- Y(n) is the estimated cold image
- M is the time constant of the filter
- a larger value of M indicates that the filtering result is greatly affected by the initial frame, and the convergence speed is slow, while a smaller value of M indicates that the filtering result is greatly affected by the current frame and the convergence speed is fast.
- priority is given to the convergence speed
- the later stage of calibration priority is given to the accuracy and stability of calibration. Since the initial value of M is set to a small value, the M value is updated every time the calibration is performed.
- the update formula of the M value is formula (9):
- M is the updated value
- M 0 is the initial value
- ⁇ M is the increment of each update
- M 0 can be 4
- ⁇ M can be 1.
- the inter-frame registration method can also be used to obtain the estimated cold image.
- the statistical histogram is used to suppress the global scene information of the image to be corrected, and the gradient information, the low-frequency component, the low-frequency information of the scene to suppress the low-frequency information, and the absolute value matrix of the difference value are used to suppress the local scene information, which can ensure that the input of the low-pass in the time domain is as little as possible.
- the introduction of scene information and thermal residue of the target object makes the estimated cold image closer to the real cold image, and the corrected image is clearer.
- the determining of the compensation matrix image according to the template cold image and the estimated cold image includes:
- Step S301 Determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel point in the preset sliding window, and obtain a correlation coefficient matrix. .
- the correlation coefficient can be obtained by using the normalized cross-correlation calculation formula, and the normalized cross-correlation calculation formula is as formula (10),
- R(i,j) is the correlation coefficient
- W is the size of the preset sliding window
- F 0 is the cold image of the template
- Y(n) is the estimated cold image, to estimate the mean of the cold image.
- the size of the preset sliding window is not specifically limited in this application, and can be set by yourself.
- the size W of the preset sliding window may be 3 ⁇ 3, or 5 ⁇ 5, or the like.
- Step S302 Determine whether each of the correlation coefficients is greater than a coefficient threshold.
- the coefficient threshold is not specifically limited in this application, and it depends on the situation.
- the coefficient threshold may be 0.7, or 0.75, and so on.
- Step S303 If the correlation coefficient is greater than the coefficient threshold, take the pixel value of the corresponding pixel in the estimated cold image as a compensation value.
- the compensation value directly takes the corresponding pixel value in the estimated cold image.
- Step S304 If the correlation coefficient is not greater than the coefficient threshold, determine the corresponding corresponding pixel in the estimated cold image, and take the pixel mean of all the pixels in the preset neighborhood of the corresponding pixel as the compensation value.
- the compensation value takes the pixel mean value of all the pixels in the preset neighborhood with the corresponding pixel as the center.
- Step S305 The compensation value constitutes the compensation matrix image.
- the following describes the infrared image correction device provided by the embodiments of the present application.
- the infrared image correction device described below and the infrared image correction method described above can be referred to each other correspondingly.
- FIG. 5 is a structural block diagram of an apparatus for correcting an infrared image provided by an embodiment of the present application.
- the apparatus for correcting an infrared image may include:
- the first acquisition module 100 is configured to acquire multiple frames of images collected by a refrigeration infrared imaging system under preset influencing factors, and the multiple frames of images have a cold reflection phenomenon;
- the second acquisition module 200 is configured to acquire the to-be-corrected image collected by the refrigeration infrared imaging system
- the judgment module 300 is used for judging the picture state of the to-be-corrected image, and the picture state includes a static state and a motion state;
- the correction module 400 is configured to correct the to-be-corrected image by using the multi-frame images according to the picture state to obtain a corrected image.
- the infrared image correction apparatus of this embodiment is used to realize the aforementioned infrared image correction method, so the specific implementation of the infrared image correction apparatus can be found in the foregoing example section of the infrared image correction method, for example, the first acquisition
- the module 100, the second acquisition module 200, the judgment module 300, and the correction module 400 are respectively used to implement steps S101, S102, S103 and S104 in the above-mentioned infrared image correction method, so the specific implementation can be implemented with reference to the corresponding parts.
- the description of the example will not be repeated here.
- the infrared image correction device in the present application determines whether the image state of the image to be corrected is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different
- the multi-frame image of the reflection phenomenon is used to correct the image to be corrected, eliminating the cold reflection phenomenon in the image to be corrected, improving the image quality, and without improving the optical design of the cooling infrared imaging system, which is very simple and easy to implement.
- the correction module 400 includes:
- a first determination submodule configured to select, from the multi-frame images, the multi-frame images that are closest to the collection environment information of the to-be-corrected image as a compensation matrix when the picture state is the static state image;
- the second determination sub-module is configured to perform weighting processing on the multi-frame images when the picture state is the motion state to obtain a template cold image; perform scene suppression processing on the to-be-corrected image to obtain an estimated cold image ; According to the template cold image and the estimated cold image, determine the compensation matrix image;
- a correction sub-module configured to correct the to-be-corrected image according to the compensation matrix image to obtain the corrected image.
- the second determination submodule includes:
- a first determining unit configured to determine a background pixel value according to the to-be-corrected image
- a first calculation unit used to calculate the absolute value of the difference between the pixel value of each pixel in the to-be-corrected image and the background pixel value to obtain an absolute value matrix
- a second determining unit configured to determine the confidence level of each absolute value of the difference in the absolute value matrix, and obtain a confidence level matrix for suppressing the overall scene information
- a third determining unit configured to determine the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;
- a fourth determining unit configured to determine the low-frequency component suppressed by the local scene information according to the low-frequency information and the gradient information;
- a fifth determining unit configured to determine scene suppression low-frequency information according to the confidence matrix and the low-frequency component
- the second calculation unit is configured to input the scene suppression low-frequency information into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
- the second determining unit includes:
- a first judging subunit configured to judge whether the absolute value of the difference is less than a first preset threshold
- a first determination subunit configured to determine the confidence level of the absolute value of the difference as a first confidence level if the absolute value of the difference is less than a first preset threshold
- a second judging subunit configured to judge whether the absolute value of the difference is greater than a second preset threshold if the absolute value of the difference is not less than a first preset threshold; the second preset threshold is greater than the first preset threshold a preset threshold;
- a second determination subunit configured to determine the confidence level of the absolute value of the difference as the second confidence level if the absolute value of the difference is greater than the second preset threshold; the first confidence level is greater than the second confidence level;
- a third determination subunit configured to determine the confidence level of the absolute value of the difference according to a preset function if the absolute value of the difference is not greater than the second preset threshold.
- the second determination submodule includes:
- the sixth determination unit is used to determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel point in the preset sliding window, and obtain a correlation coefficient matrix
- a judging unit for judging whether each of the correlation coefficients is greater than a coefficient threshold
- a seventh determination unit configured to take the pixel value of the corresponding pixel in the estimated cold image as a compensation value if the correlation coefficient is greater than the coefficient threshold
- an eighth determination unit configured to determine the corresponding corresponding pixel in the estimated cold image if the correlation coefficient is not greater than the coefficient threshold, and take all the pixels in the preset neighborhood of the corresponding pixel The pixel mean value of is used as the compensation value;
- the second determination sub-module includes a processing unit, and the processing unit is specifically configured to perform an average weighting process on the multiple frames of images to obtain the template cold image.
- the judging module 300 is specifically configured to judge the picture state of the image to be corrected by using a frame difference method or a positioning method.
- the infrared image correction device provided by the embodiments of the present application is introduced below, and the infrared image correction device described below and the infrared image correction method described above can be referred to each other correspondingly.
- An infrared image correction device comprising:
- the processor is configured to implement the steps of any one of the above-mentioned infrared image correction methods when executing the computer program.
- the following describes the infrared image correction system provided by the embodiments of the present application.
- the infrared image correction system described below and the infrared image correction method described above can be referred to each other correspondingly.
- FIG. 6 is a schematic structural diagram of a refrigeration infrared imaging system provided by an embodiment of the present application.
- a refrigerated infrared imaging system includes the above-mentioned infrared image correction device.
- the refrigeration infrared imaging system further includes optical equipment, refrigeration detectors, power supply equipment, temperature control equipment, display, and image processing controller, wherein the infrared image correction equipment is connected with the image processing controller.
- a software module can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.
- RAM random access memory
- ROM read only memory
- electrically programmable ROM electrically erasable programmable ROM
- registers hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.
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Abstract
A method, apparatus and device (7) for correcting an infrared image, and a refrigeration infrared imaging system. The method comprises: acquiring a plurality of frames of image, in which a cold reflection phenomenon occurs, which are collected by a refrigeration infrared imaging system (S101); acquiring an image to be corrected collected by the refrigeration infrared imaging system (S102); determining a picture state of the image to be corrected, wherein the picture states comprise a static state and a moving state (S103); and correcting, according to the picture state, the image to be corrected by using the plurality of frames of image, so as to obtain a corrected image (S104). A plurality of frames of image in which a cold reflection phenomenon occurs, and an image to be corrected are obtained, and whether a picture state of the image to be corrected is a static state or a moving state is determined, and then the image to be corrected is corrected according to different picture states and by using the plurality of frames of image in which the cold reflection phenomenon occurs, so as to eliminate a cold reflection phenomenon in the image to be corrected, thereby improving the image quality. Moreover, the optical design of a refrigeration infrared imaging system does not need to be improved, and the system is very simple and easily implemented.
Description
本申请要求于2020年08月27日提交中国专利局、申请号为202010879910.7、发明名称为“红外图像的校正方法、装置、设备及制冷红外成像系统”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese patent application filed on August 27, 2020 with the application number 202010879910.7 and the invention titled "Infrared Image Correction Method, Device, Equipment and Refrigeration Infrared Imaging System", the entire contents of which are Incorporated herein by reference.
本申请涉及红外成像技术领域,特别是涉及一种红外图像的校正方法、装置、设备及制冷红外成像系统。The present application relates to the technical field of infrared imaging, and in particular, to an infrared image correction method, device, equipment and refrigeration infrared imaging system.
自然界中任何温度高于绝对零度的物体都不停地发射红外热辐射,红外成像技术就是利用物体发射的红外辐射,将其转变为视觉可分辨的图像,并可以进一步计算出温度值。制冷红外成像系统由于具有探测距离远、灵敏度高、探测能力强等优点,广泛应用在安防、农业、工业等领域。Any object with a temperature higher than absolute zero in nature continuously emits infrared thermal radiation. Infrared imaging technology uses the infrared radiation emitted by the object to convert it into a visually distinguishable image, and can further calculate the temperature value. Refrigerated infrared imaging system is widely used in security, agriculture, industry and other fields due to its advantages of long detection distance, high sensitivity and strong detection ability.
制冷红外探测器在工作时处于低温腔内,低温腔内的温度一般在90K~110K之间,而整个红外成像系统的工作环境温度通常在300K左右,所以制冷红外探测器接收到的红外辐射不仅包括观测目标,还包括红外成像系统内部器件,如镜筒、镜头等,呈现在图像上表现为亮环和冷斑,即冷反射效应。冷反射效应在制冷红外成像系统中普遍存在,严重影响红外成像系统的探测、识别能力和用户的观感体验。为了抑制冷反射效应,目前一般从光学方面抑制或者采用非均匀性校正算法,光学抑制需要在光学设计、镀膜上做大量工作,难度大、成本高,工程化困难;而非均匀校正算法并不能对冷反射进行完全修正,图像中仍会出现斑点。The refrigerated infrared detector is in a low temperature cavity when working, and the temperature in the low temperature cavity is generally between 90K and 110K, and the working environment temperature of the entire infrared imaging system is usually around 300K, so the infrared radiation received by the refrigerated infrared detector is not only Including the observation target, but also the internal components of the infrared imaging system, such as the lens barrel, lens, etc., appear on the image as bright rings and cold spots, that is, the cold reflection effect. The cold reflection effect is common in the refrigeration infrared imaging system, which seriously affects the detection and recognition ability of the infrared imaging system and the user's perception experience. In order to suppress the cold reflection effect, the non-uniformity correction algorithm is generally suppressed or used from the optical aspect. Optical suppression requires a lot of work in optical design and coating, which is difficult, costly, and difficult to engineer. The non-uniformity correction algorithm cannot With full correction of cold reflections, speckles will still appear in the image.
因此,如何解决上述技术问题应是本领域技术人员重点关注的。Therefore, how to solve the above technical problems should be the focus of those skilled in the art.
发明内容SUMMARY OF THE INVENTION
本申请的目的是提供一种红外图像的校正方法、装置、设备及制冷红外成像系统,以提升红外图像的质量,且简单易行。The purpose of the present application is to provide an infrared image correction method, device, equipment and refrigeration infrared imaging system, so as to improve the quality of the infrared image, which is simple and easy to implement.
为解决上述技术问题,本申请提供一种红外图像的校正方法,包括:In order to solve the above-mentioned technical problems, the present application provides a method for calibrating an infrared image, including:
获取制冷红外成像系统采集的具有冷反射现象的多帧图像;Acquiring multiple frames of images with cold reflection phenomenon collected by the cooling infrared imaging system;
获取所述制冷红外成像系统采集的待校正图像;acquiring the to-be-corrected image collected by the refrigeration infrared imaging system;
判断所述待校正图像的画面状态;所述画面状态包括静止状态和运动状态;Judging the picture state of the image to be corrected; the picture state includes a static state and a motion state;
根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。According to the picture state, the to-be-corrected image is corrected by using the multi-frame images to obtain a corrected image.
可选的,所述根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像包括:Optionally, according to the picture state, using the multi-frame images to correct the image to be corrected, and obtaining the corrected image includes:
当所述画面状态为所述静止状态时,从所述多帧图像中选取与所述待校正图像的采集环境信息最接近的所述多帧图像,作为补偿矩阵图像;When the picture state is the static state, selecting the multi-frame image from the multi-frame images that is closest to the collection environment information of the to-be-corrected image as a compensation matrix image;
当所述画面状态为所述运动状态时,对所述多帧图像进行加权处理,得到模板冷像;对所述待校正图像进行场景抑制处理,得到估计冷像;根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像;When the picture state is the motion state, weighting processing is performed on the multi-frame images to obtain a template cold image; scene suppression processing is performed on the to-be-corrected image to obtain an estimated cold image; according to the template cold image and the estimated cold image, to determine the compensation matrix image;
根据所述补偿矩阵图像对所述待校正图像进行校正,得到所述校正后图像。Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.
可选的,所述对所述待校正图像进行场景抑制处理,得到估计冷像包括:Optionally, performing scene suppression processing on the to-be-corrected image to obtain an estimated cold image includes:
根据所述待校正图像,确定背景像素值;determining a background pixel value according to the to-be-corrected image;
计算所述待校正图像中每个像素点的像素值与所述背景像素值的差值绝对值,得到绝对值矩阵;Calculate the absolute value of the difference between the pixel value of each pixel in the to-be-corrected image and the background pixel value to obtain an absolute value matrix;
确定所述绝对值矩阵中每个所述差值绝对值的置信度,得到整体场景信息抑制的置信度矩阵;determining the confidence level of each absolute value of the difference in the absolute value matrix to obtain a confidence level matrix for suppressing the overall scene information;
确定所述待校正图像的低频信息和所述待校正图像中每个像素点的梯度信息;determining the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;
根据所述低频信息和所述梯度信息确定局部场景信息抑制的低频分量;Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information;
根据所述置信度矩阵和所述低频分量确定场景抑制低频信息;determining the scene suppression low-frequency information according to the confidence matrix and the low-frequency component;
将所述场景抑制低频信息输入时域低通滤波算法公式,得到所述估计冷像。The low-frequency information of the scene suppression is input into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
可选的,所述确定所述绝对值矩阵中每个所述差值绝对值的置信度,包括:Optionally, the determining the confidence level of each absolute value of the difference in the absolute value matrix includes:
判断所述差值绝对值是否小于第一预设阈值;judging whether the absolute value of the difference is less than a first preset threshold;
若所述差值绝对值小于第一预设阈值,则确定所述差值绝对值的所述置信度为第一置信度;If the absolute value of the difference is smaller than the first preset threshold, determining the confidence of the absolute value of the difference as the first confidence;
若所述差值绝对值不小于第一预设阈值,则判断所述差值绝对值是否大于第二预设阈值;所述第二预设阈值大于所述第一预设阈值;If the absolute value of the difference is not less than the first preset threshold, determine whether the absolute value of the difference is greater than a second preset threshold; the second preset threshold is greater than the first preset threshold;
若所述差值绝对值大于所述第二预设阈值,则确定所述差值绝对值的所述置信度为第二置信度;所述第一置信度大于所述第二置信度;If the absolute value of the difference is greater than the second preset threshold, the confidence level of the absolute value of the difference is determined to be the second confidence level; the first confidence level is greater than the second confidence level;
若所述差值绝对值不大于所述第二预设阈值,则根据预设函数确定所述差值绝对值的所述置信度。If the absolute value of the difference is not greater than the second preset threshold, the confidence level of the absolute value of the difference is determined according to a preset function.
可选的,所述根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像包括:Optionally, determining the compensation matrix image according to the template cold image and the estimated cold image includes:
确定所述模板冷像和所述估计冷像在预设滑动窗口内中心像素点的相关系数,并得到相关系数矩阵;Determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel in the preset sliding window, and obtain a correlation coefficient matrix;
判断每个所述相关系数是否大于系数阈值;Judging whether each of the correlation coefficients is greater than a coefficient threshold;
若所述相关系数大于所述系数阈值,则在所述估计冷像中取对应像素点的像素值作为补偿值;If the correlation coefficient is greater than the coefficient threshold, taking the pixel value of the corresponding pixel in the estimated cold image as a compensation value;
若所述相关系数不大于所述系数阈值,则在所述估计冷像中确定对应的对应像素点,并取所述对应像素点的预设邻域内的所有像素点的像素均值作为所述补偿值;If the correlation coefficient is not greater than the coefficient threshold, determine the corresponding corresponding pixel in the estimated cold image, and take the pixel mean value of all the pixels in the preset neighborhood of the corresponding pixel as the compensation value;
所述补偿值构成所述补偿矩阵图像。The compensation values constitute the compensation matrix image.
可选的,所述对所述多帧图像进行加权处理,得到模板冷像包括:Optionally, performing weighting processing on the multi-frame images to obtain a cold template image includes:
对所述多帧图像进行平均加权处理,得到所述模板冷像。Perform an average weighting process on the multiple frames of images to obtain the template cold image.
可选的,所述判断所述待校正图像的画面状态包括:Optionally, the judging the picture state of the image to be corrected includes:
利用帧差法或者定位法判断所述待校正图像的所述画面状态。The frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
本申请还提供一种红外图像的校正装置,包括:The present application also provides an infrared image correction device, comprising:
第一获取模块,用于获取制冷红外成像系统在预设影响因素下采集的多帧图像,所述多帧图像中具有冷反射现象;a first acquisition module, configured to acquire multiple frames of images collected by the cooling infrared imaging system under preset influencing factors, and the multiple frames of images have cold reflection phenomenon;
第二获取模块,用于获取所述制冷红外成像系统采集的待校正图像;a second acquisition module, configured to acquire the to-be-corrected image collected by the refrigeration infrared imaging system;
判断模块,用于判断所述待校正图像的画面状态,所述画面状态包括静止状态和运动状态;a judgment module for judging the picture state of the image to be corrected, where the picture state includes a static state and a motion state;
校正模块,用于根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。A correction module, configured to correct the image to be corrected by using the multi-frame images according to the picture state to obtain a corrected image.
本申请还提供一种红外图像校正设备,包括:The application also provides an infrared image correction device, including:
存储器,用于存储计算机程序;memory for storing computer programs;
处理器,用于执行所述计算机程序时实现上述任一种所述红外图像的校正方法的步骤。The processor is configured to implement the steps of any one of the above-mentioned infrared image correction methods when executing the computer program.
本申请还提供一种制冷红外成像系统,所述制冷红外成像系统包括上述所述的红外图像校正设备。The present application further provides a refrigerated infrared imaging system, the refrigerated infrared imaging system includes the above-mentioned infrared image correction device.
本申请所提供的一种红外图像的校正方法,包括获取制冷红外成像系统采集的具有冷反射现象的多帧图像;获取所述制冷红外成像系统采集的待校正图像;判断所述待校正图像的画面状态;所述画面状态包括静止状态和运动状态;根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。A method for calibrating an infrared image provided by the present application includes acquiring multiple frames of images with cold reflections collected by a cooling infrared imaging system; obtaining an image to be corrected collected by the cooling infrared imaging system; The picture state; the picture state includes a static state and a motion state; according to the picture state, the image to be corrected is corrected by using the multi-frame images to obtain a corrected image.
可见,本申请中的红外图像校正方法,通过获得具有冷反射现象的多帧图像和待校正的图像,确定待校正图像的画面状态是处于静止状态还是运动状态,进而根据不同的画面状态,利用具有冷反射现象的多帧图像对待校正图像进行校正,消除待校正图像中的冷反射现象,提升图像质量,并且无需改进制冷红外成像系统的光学设计,非常简单易行。It can be seen that the infrared image correction method in the present application determines whether the picture state of the to-be-corrected image is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different picture states, use It is very simple and easy to correct the image to be corrected for the multi-frame images with cold reflection phenomenon, eliminate the cold reflection phenomenon in the to-be-corrected image, improve the image quality, and do not need to improve the optical design of the cooling infrared imaging system.
此外,本申请还提供一种具有上述优点的校正装置、设备及制冷红外成像系统。In addition, the present application also provides a correction device, equipment and refrigeration infrared imaging system with the above advantages.
为了更清楚的说明本申请实施例或现有技术的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单的介绍,显而易见地,下 面描述中的附图仅仅是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions of the embodiments of the present application or the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only For some embodiments of the present application, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.
图1为本申请实施例所提供的一种红外图像的校正方法的流程图;1 is a flowchart of a method for calibrating an infrared image provided by an embodiment of the present application;
图2为模拟目标物体的辐射温度采集多帧图像时的示意图;2 is a schematic diagram of simulating the radiation temperature of a target object and collecting multiple frames of images;
图3为本申请实施例所提供的得到估计冷像的流程图;3 is a flowchart of obtaining an estimated cold image provided by an embodiment of the present application;
图4为本申请实施例所提供的确定补偿矩阵图像的流程图;4 is a flowchart of determining a compensation matrix image provided by an embodiment of the present application;
图5为本申请实施例提供的红外图像的校正装置的结构框图;FIG. 5 is a structural block diagram of an infrared image correction apparatus provided by an embodiment of the present application;
图6为本申请实施例提供的制冷红外成像系统的结构示意图。FIG. 6 is a schematic structural diagram of a cooling infrared imaging system provided by an embodiment of the present application.
为了使本技术领域的人员更好地理解本申请方案,下面结合附图和具体实施方式对本申请作进一步的详细说明。显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。In order to make those skilled in the art better understand the solution of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
正如背景技术部分所述,目前,一般从光学方面抑制或者采用非均匀性校正算法,光学抑制需要在光学设计、镀膜上做大量工作,难度大、成本高,工程化困难;而非均匀校正算法并不能对冷反射进行完全修正,图像中仍会出现斑点。As mentioned in the background section, at present, the non-uniformity correction algorithm is generally suppressed or used from the optical aspect. Optical suppression requires a lot of work on optical design and coating, which is difficult, expensive, and difficult to engineer; the non-uniformity correction algorithm Cold reflections are not fully corrected, and speckles will still appear in the image.
有鉴于此,本申请提供一种红外图像的校正方法,请参考图1,图1为本申请实施例所提供的一种红外图像的校正方法的流程图,该方法包括:In view of this, the present application provides a method for calibrating an infrared image. Please refer to FIG. 1. FIG. 1 is a flowchart of a method for calibrating an infrared image provided by an embodiment of the present application, and the method includes:
步骤S101:获取制冷红外成像系统采集的具有冷反射现象的多帧图像。Step S101 : acquiring multiple frames of images with cold reflection phenomenon collected by a cooling infrared imaging system.
当制冷红外成像系统搭建完成后,影响冷反射强度的部分因素已经确定,如工作的红外波段范围、光学元件的有效通光孔径、红外镜筒折射面的反射率等,因此,在采集具有冷反射现象的多帧图像的过程中可只考虑环境温度、目标物体的辐射温度和焦距这三个影响因素,对于这三个影响因素采用单一变量原则,采集多帧图像。环境温度范围、温度间隔和目标物体的辐射温度范围和温度间隔可按实际情况设置,例如温度范围可均取-25℃~35℃,温度间隔均取ΔT=5℃,其中,当需改变目标物体的辐射温度 时,目标物体的辐射温度通过黑体温度进行模拟,具体请参见图2。After the cooling infrared imaging system is built, some factors affecting the intensity of cold reflection have been determined, such as the working infrared wavelength range, the effective clear aperture of optical components, and the reflectivity of the infrared lens barrel refracting surface. In the process of multi-frame images of the reflection phenomenon, only three influencing factors can be considered: the ambient temperature, the radiation temperature of the target object, and the focal length. For these three influencing factors, the single-variable principle is used to collect multiple frames of images. The ambient temperature range, temperature interval, and the radiation temperature range and temperature interval of the target object can be set according to the actual situation. For example, the temperature range can be taken from -25℃ to 35℃, and the temperature interval can be taken as ΔT=5℃. When the radiation temperature of the object is used, the radiation temperature of the target object is simulated by the black body temperature, see Figure 2 for details.
需要说明的是,具有冷反射现象的多帧图像可以是预先采集保存的,也可以是实时采集的,本申请中不做具体限定。It should be noted that the multi-frame images with the cold reflection phenomenon may be pre-collected and saved, or may be collected in real time, which is not specifically limited in this application.
步骤S102:获取所述制冷红外成像系统采集的待校正图像。Step S102 : acquiring the to-be-corrected image collected by the cooling infrared imaging system.
需要指出的是,待校正图像为连续帧图像。It should be pointed out that the images to be corrected are consecutive frame images.
步骤S103:判断所述待校正图像的画面状态;所述画面状态包括静止状态和运动状态。Step S103: Determine the picture state of the image to be corrected; the picture state includes a static state and a motion state.
可选的,所述判断所述待校正图像的画面状态包括:Optionally, the judging the picture state of the image to be corrected includes:
利用帧差法或者定位法判断所述待校正图像的所述画面状态。The frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
具体的,采用帧差法时,对相邻两帧待校正图像进行差分运算,不同帧对应的像素点相减,得到灰度差的绝对值,当绝对值的和超过预设阈值时,即可判断待校正图像的画面状态为运动状态,否则为静止状态。在吊舱和周扫的应用场景下,还可以利用定位法,例如飞机定位系统GPS判断位置是否发生移动,若发生移动,则为运动状态,否则为静止状态。需要指出的是,还可以采用运动传感器,例如陀螺仪,判断位置是否发生移动,进而判定画面状态是静止状态还是运动状态。Specifically, when the frame difference method is adopted, a difference operation is performed on the images to be corrected in two adjacent frames, and the pixels corresponding to different frames are subtracted to obtain the absolute value of the grayscale difference. When the sum of the absolute values exceeds the preset threshold, that is, It can be judged that the picture state of the image to be corrected is a moving state, otherwise it is a static state. In the application scenarios of pods and weekly sweeps, positioning methods can also be used, such as the aircraft positioning system GPS to determine whether the position has moved. It should be pointed out that a motion sensor, such as a gyroscope, may also be used to determine whether the position has moved, and then determine whether the screen state is a static state or a moving state.
步骤S104:根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。Step S104: According to the picture state, correct the image to be corrected by using the multi-frame images to obtain a corrected image.
可选的,所述根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像包括:Optionally, according to the picture state, using the multi-frame images to correct the image to be corrected, and obtaining the corrected image includes:
步骤S1041:当所述画面状态为所述静止状态时,从所述多帧图像中选取与所述待校正图像的采集环境信息最接近的所述多帧图像,作为补偿矩阵图像。Step S1041 : when the picture state is the static state, select the multi-frame image that is closest to the collection environment information of the to-be-corrected image from the multi-frame images as a compensation matrix image.
采集环境信息即指采集具有冷反射现象的多帧图像的过程中的影响因素,即环境温度、目标物体的辐射温度和焦距。Collecting environmental information refers to the influencing factors in the process of collecting multiple frames of images with cold reflection, that is, the environmental temperature, the radiation temperature of the target object, and the focal length.
步骤S1042:当所述画面状态为所述运动状态时,对所述多帧图像进行加权处理,得到模板冷像;对所述待校正图像进行场景抑制处理,得到估计冷像;根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像。Step S1042: when the picture state is the motion state, perform weighting processing on the multi-frame images to obtain a template cold image; perform scene suppression processing on the to-be-corrected image to obtain an estimated cold image; according to the template The cold image and the estimated cold image determine the compensation matrix image.
可选的,所述对所述多帧图像进行加权处理,得到模板冷像包括:Optionally, performing weighting processing on the multi-frame images to obtain a cold template image includes:
对所述多帧图像进行平均加权处理,得到所述模板冷像。Perform an average weighting process on the multiple frames of images to obtain the template cold image.
具体的,模板冷像的计算公式如式(1),Specifically, the calculation formula of the template cold image is as formula (1),
式中,F
0为模板冷像,F
a为多帧图像中第a帧图像,k
a为第a帧图像的权重系数。
In the formula, F 0 is the cold image of the template, F a is the a-th frame image in the multi-frame image, and ka is the weight coefficient of the a-th frame image.
步骤S1043:根据所述补偿矩阵图像对所述待校正图像进行校正,得到所述校正后图像。Step S1043: Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.
具体的,利用待校正图像的像素值减去补偿矩阵图像对应的像素值,得到校正后图像。Specifically, the corrected image is obtained by subtracting the pixel value corresponding to the compensation matrix image from the pixel value of the image to be corrected.
本申请中的红外图像校正方法,通过获得具有冷反射现象的多帧图像和待校正的图像,确定待校正图像的画面状态是处于静止状态还是运动状态,进而根据不同的画面状态,利用具有冷反射现象的多帧图像对待校正图像进行校正,消除待校正图像中的冷反射现象,提升图像质量,并且无需改进制冷红外成像系统的光学设计,非常简单易行。The infrared image correction method in the present application determines whether the image state of the image to be corrected is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different The multi-frame image of the reflection phenomenon is used to correct the image to be corrected, eliminating the cold reflection phenomenon in the image to be corrected, improving the image quality, and without improving the optical design of the cooling infrared imaging system, which is very simple and easy to implement.
在上述实施例的基础上,在本申请的一个实施例中,请参考图3,所述对所述待校正图像进行场景抑制处理,得到估计冷像包括:On the basis of the above embodiment, in an embodiment of the present application, please refer to FIG. 3 , the scene suppression processing on the to-be-corrected image to obtain an estimated cold image includes:
步骤S201:根据所述待校正图像,确定背景像素值。Step S201: Determine a background pixel value according to the to-be-corrected image.
具体的,利用统计直方图对待校正图像的所有像素值进行统计,在直方图中找到统计数目最多的对应的像素响应值作为整体场景信息抑制的背景像素值I
b,统计数目可以为像素值在待校正图像中出现的次数,统计直方图示意图如图4所示。
Specifically, the statistical histogram is used to perform statistics on all pixel values of the image to be corrected, and the corresponding pixel response value with the largest number of statistics is found in the histogram as the background pixel value I b suppressed by the overall scene information. The number of occurrences in the to-be-corrected image, and the schematic diagram of the statistical histogram is shown in Figure 4.
步骤S202:计算所述待校正图像中每个像素点的像素值与所述背景像素值的差值绝对值,得到绝对值矩阵。Step S202: Calculate the absolute value of the difference between the pixel value of each pixel point in the image to be corrected and the background pixel value to obtain an absolute value matrix.
具体的,计算到绝对值矩阵的公式如式(2),Specifically, the formula for calculating the absolute value matrix is as formula (2),
D(n)=abs(I-I
b) (2)
D(n)=abs(II b ) (2)
式中,I
b为背景像素值,I为待校正图像中每个像素点的像素值,D(n) 为绝对值矩阵。
In the formula, I b is the background pixel value, I is the pixel value of each pixel in the image to be corrected, and D(n) is the absolute value matrix.
步骤S203:确定所述绝对值矩阵中每个所述差值绝对值的置信度,得到整体场景信息抑制的置信度矩阵。Step S203: Determine the confidence level of each absolute value of the difference in the absolute value matrix, and obtain a confidence level matrix for suppressing the overall scene information.
可选的,所述确定所述绝对值矩阵中每个所述差值绝对值的置信度,包括:Optionally, the determining the confidence level of each absolute value of the difference in the absolute value matrix includes:
步骤S2031:判断所述差值绝对值是否小于第一预设阈值。Step S2031: Determine whether the absolute value of the difference is less than a first preset threshold.
本申请中对第一预设阈值不做具体限定,视情况而定,例如,第一预设阈值可以为100,或者120等等。The first preset threshold is not specifically limited in this application, and it depends on the situation. For example, the first preset threshold may be 100, or 120, and so on.
步骤S2032:若所述差值绝对值小于第一预设阈值,则确定所述差值绝对值的所述置信度为第一置信度。Step S2032: If the absolute value of the difference is smaller than a first preset threshold, determine the confidence level of the absolute value of the difference as a first confidence level.
本申请中对第一置信度不做具体限定,可自行设置,例如,第一置信度可以为0.9,或者0.85等等。The first confidence level is not specifically limited in this application, and can be set by yourself. For example, the first confidence level may be 0.9, or 0.85, and so on.
步骤S2033:若所述差值绝对值不小于第一预设阈值,则判断所述差值绝对值是否大于第二预设阈值;所述第二预设阈值大于所述第一预设阈值。Step S2033: If the absolute value of the difference is not less than a first preset threshold, determine whether the absolute value of the difference is greater than a second preset threshold; the second preset threshold is greater than the first preset threshold.
本申请中对第二预设阈值也不做具体限定,保证第二预设阈值大于第一预设阈值即可,例如,第二预设阈值可以为600,或者700等等。The second preset threshold is not specifically limited in this application, and it is sufficient to ensure that the second preset threshold is greater than the first preset threshold. For example, the second preset threshold may be 600, or 700, and so on.
步骤S2034:若所述差值绝对值大于所述第二预设阈值,则确定所述差值绝对值的所述置信度为第二置信度;所述第一置信度大于所述第二置信度。Step S2034: if the absolute value of the difference is greater than the second preset threshold, determine the confidence of the absolute value of the difference as a second confidence; the first confidence is greater than the second confidence Spend.
本申请中对第二置信度也不做具体限定,保证第二置信度小于第一置信度即可,例如,第二置信度可以为0.2,或者0.15等等。The second confidence level is not specifically limited in this application, and it is sufficient to ensure that the second confidence level is smaller than the first confidence level. For example, the second confidence level may be 0.2, or 0.15, and so on.
步骤S2035:若所述差值绝对值不大于所述第二预设阈值,则根据预设函数确定所述差值绝对值的所述置信度。Step S2035: If the absolute value of the difference is not greater than the second preset threshold, determine the confidence level of the absolute value of the difference according to a preset function.
具体的,预设函数请参见式(3),Specifically, please refer to formula (3) for the preset function,
式中,p(i,j)为绝对值矩阵中坐标为(i,j)的差值绝对值的置信度,p
max为第一置信度,p
min为第二置信度,Thr
max为第二预设阈值,Thr
min为第一预设阈值,D(i,j)为绝对值矩阵中坐标为(i,j)的差值绝对值。
In the formula, p(i,j) is the confidence level of the absolute value of the difference in the absolute value matrix whose coordinates are (i,j), pmax is the first confidence level, pmin is the second confidence level, and Thrmax is the first confidence level. Two preset thresholds, Thr min is the first preset threshold, and D(i, j) is the absolute value of the difference with the coordinates (i, j) in the absolute value matrix.
置信度实际上是分段映射函数,如式(4),分段映射函数关系图如图5所示,Confidence is actually a piecewise mapping function, as shown in Equation (4). The piecewise mapping function diagram is shown in Figure 5.
绝对值矩阵中差值绝对值越大的点,表示该点为过暗或过亮目标的概率越大,该点对估计冷像的干扰越大,置信度也就越低。为避免过亮、过暗目标干扰,将差值绝对值超过第二预设阈值的点设置为较小的第二置信度,一般场景信息的灰度具有一定的波动,因此将差值绝对值小于较小的第一预设阈值的点设置为较大的第一置信度。A point with a larger absolute value of the difference in the absolute value matrix indicates a higher probability that the point is a too dark or too bright target, the greater the interference of the point to the estimated cold image, and the lower the confidence. In order to avoid the interference of objects that are too bright or too dark, the point where the absolute value of the difference exceeds the second preset threshold is set to a smaller second confidence level. Generally, the gray level of the scene information has certain fluctuations, so the absolute value of the difference is set as the second confidence level. Points smaller than the smaller first preset threshold are set as larger first confidence levels.
步骤S204:确定所述待校正图像的低频信息和所述待校正图像中每个像素点的梯度信息。Step S204: Determine low-frequency information of the image to be corrected and gradient information of each pixel in the image to be corrected.
可选的,低频信息I
low的计算方法可以采用但不限于K*K均值滤波方法,其中,K可以取3。
Optionally, the calculation method of the low-frequency information I low may be, but not limited to, the K*K mean filtering method, where K may be 3.
具体的,梯度信息计算公式如式(5),Specifically, the gradient information calculation formula is as formula (5),
式中,G(i,j)为(i,j)像素点的梯度信息,I(i,j)为(i,j)像素点的像素值,I(i+1,j)为(i+1,j)像素点的像素值,I(i,j+1)为(i,j+1)像素点的像素值。In the formula, G(i,j) is the gradient information of the (i,j) pixel, I(i,j) is the pixel value of the (i,j) pixel, and I(i+1,j) is the (i) +1,j) The pixel value of the pixel point, I(i,j+1) is the pixel value of the (i,j+1) pixel point.
由于一些边缘类的场景信息和邻域信息差异很大,因此边缘处的信息与邻域差异仍然较大,计算梯度信息的目的是避免局部边缘信息对估计冷像的干扰。Since the scene information and neighborhood information of some edge classes are very different, the information at the edge is still quite different from the neighborhood information. The purpose of calculating the gradient information is to avoid the interference of local edge information on the estimated cold image.
步骤S205:根据所述低频信息和所述梯度信息确定局部场景信息抑制的低频分量。Step S205: Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information.
具体的,低频分量计算公式如式(6),Specifically, the low-frequency component calculation formula is as in Equation (6),
式中,Low为低频分量,I
low为低频信息,G为梯度信息。
In the formula, Low is the low-frequency component, I low is the low-frequency information, and G is the gradient information.
低频分量的作用是对待校正图像的局部边缘和纹理信息进行加强抑 制。The function of the low frequency component is to strengthen the suppression of the local edge and texture information of the image to be corrected.
步骤S206:根据所述置信度矩阵和所述低频分量确定场景抑制低频信息。Step S206: Determine scene suppression low-frequency information according to the confidence matrix and the low-frequency component.
具体的,场景抑制低频信息计算公式如式(7),Specifically, the calculation formula of scene suppression low-frequency information is as formula (7),
X=Low×p (7)X=Low×p (7)
式中,X为场景抑制低频信息,Low为低频分量,p为置信度矩阵。In the formula, X is the scene suppression low-frequency information, Low is the low-frequency component, and p is the confidence matrix.
步骤S207:将所述场景抑制低频信息输入时域低通滤波算法公式,得到所述估计冷像。Step S207: Inputting the low-frequency information of the scene suppression into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
具体的,时域低通滤波算法公式如式(8),Specifically, the time-domain low-pass filtering algorithm formula is as shown in Equation (8),
式中,n为具有冷反射现象的多帧图像的数量,X(n)为第n帧多帧图像的场景抑制低频信息,Y(n)为估计冷像,M为滤波器的时间常数,M值较大则表明滤波结果受到初始帧的影响较大,收敛速度慢,M值较小则表明滤波结果受到当前帧的影响较大,收敛速度快。在校正初期优先考虑收敛速度,校正后期优先考虑校正的精度和稳定性,因处M初始值设置较小,每校正一次更新一次M值,M值的更新公式为式(9):In the formula, n is the number of multi-frame images with cold reflection phenomenon, X(n) is the scene suppression low-frequency information of the n-th multi-frame image, Y(n) is the estimated cold image, M is the time constant of the filter, A larger value of M indicates that the filtering result is greatly affected by the initial frame, and the convergence speed is slow, while a smaller value of M indicates that the filtering result is greatly affected by the current frame and the convergence speed is fast. In the early stage of calibration, priority is given to the convergence speed, and in the later stage of calibration, priority is given to the accuracy and stability of calibration. Since the initial value of M is set to a small value, the M value is updated every time the calibration is performed. The update formula of the M value is formula (9):
M=M
0+ΔM (9)
M=M 0 +ΔM (9)
式中,M为更新后数值,M
0为初始值,ΔM为每次更新的增量,其中,M
0可以为4,ΔM可以为1。
In the formula, M is the updated value, M 0 is the initial value, ΔM is the increment of each update, where M 0 can be 4, and ΔM can be 1.
需要指出的是,除了采用时域低通滤波算法得到估计冷像,还可以采用帧间配准法得到估计冷像。It should be pointed out that, in addition to using the temporal low-pass filtering algorithm to obtain the estimated cold image, the inter-frame registration method can also be used to obtain the estimated cold image.
本实施例中利用统计直方图对待校正图像进行全局场景信息抑制,利用梯度信息、低频分量、场景抑制低频信息、差值绝对值矩阵进行局部场景信息抑制,可以保证时域低通的输入尽量少的引入场景信息和目标物体热残留,从而使估计冷像与真实冷像更加接近,校正后图像更加清晰。In this embodiment, the statistical histogram is used to suppress the global scene information of the image to be corrected, and the gradient information, the low-frequency component, the low-frequency information of the scene to suppress the low-frequency information, and the absolute value matrix of the difference value are used to suppress the local scene information, which can ensure that the input of the low-pass in the time domain is as little as possible. The introduction of scene information and thermal residue of the target object makes the estimated cold image closer to the real cold image, and the corrected image is clearer.
在上述实施例的基础上,在本申请的一个实施例中,请参考图4,所述 根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像包括:On the basis of the above embodiment, in an embodiment of the present application, please refer to FIG. 4 , the determining of the compensation matrix image according to the template cold image and the estimated cold image includes:
步骤S301:确定所述模板冷像和所述估计冷像在预设滑动窗口内中心像素点的相关系数,并得到相关系数矩阵。。Step S301: Determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel point in the preset sliding window, and obtain a correlation coefficient matrix. .
相关系数可以采用归一化互相关计算公式得到,归一化互相关计算公式如式(10),The correlation coefficient can be obtained by using the normalized cross-correlation calculation formula, and the normalized cross-correlation calculation formula is as formula (10),
式中,R(i,j)为相关系数,W为预设滑动窗口的尺寸,F
0为模板冷像,
为模板冷像均值,Y(n)为估计冷像,
为估计冷像均值。
where R(i,j) is the correlation coefficient, W is the size of the preset sliding window, F 0 is the cold image of the template, is the mean value of the template cold image, Y(n) is the estimated cold image, to estimate the mean of the cold image.
需要指出的是,本申请中对预设滑动窗口的尺寸不做具体限定,可自行设置。例如,预设滑动窗口的尺寸W可以为3×3,或者5×5等。随着预设滑动窗口的滑动,每次均可以得到一个相关系数,进而得到整帧模板冷像和估计冷像对应的相关系数矩阵。It should be pointed out that the size of the preset sliding window is not specifically limited in this application, and can be set by yourself. For example, the size W of the preset sliding window may be 3×3, or 5×5, or the like. With the sliding of the preset sliding window, a correlation coefficient can be obtained each time, and then the correlation coefficient matrix corresponding to the cold image of the whole frame template and the estimated cold image can be obtained.
步骤S302:判断每个所述相关系数是否大于系数阈值。Step S302: Determine whether each of the correlation coefficients is greater than a coefficient threshold.
需要说明的是,本申请中对系数阈值不做具体限定,视情况而定,例如,系数阈值可以为0.7,或者0.75等等。It should be noted that the coefficient threshold is not specifically limited in this application, and it depends on the situation. For example, the coefficient threshold may be 0.7, or 0.75, and so on.
步骤S303:若所述相关系数大于所述系数阈值,则在所述估计冷像中取对应像素点的像素值作为补偿值。Step S303: If the correlation coefficient is greater than the coefficient threshold, take the pixel value of the corresponding pixel in the estimated cold image as a compensation value.
当相关系数大于系数阈值时,表明估计冷像准确,所以补偿值直接取估计冷像中对应的像素值。When the correlation coefficient is greater than the coefficient threshold, it indicates that the estimated cold image is accurate, so the compensation value directly takes the corresponding pixel value in the estimated cold image.
步骤S304:若所述相关系数不大于所述系数阈值,则在所述估计冷像中确定对应的对应像素点,并取所述对应像素点的预设邻域内的所有像素点的像素均值作为所述补偿值。Step S304: If the correlation coefficient is not greater than the coefficient threshold, determine the corresponding corresponding pixel in the estimated cold image, and take the pixel mean of all the pixels in the preset neighborhood of the corresponding pixel as the compensation value.
当相关系数不大于系数阈值时,补偿值取以对应像素点为中心的预设邻域内所有像素点的像素均值。其中When the correlation coefficient is not greater than the coefficient threshold, the compensation value takes the pixel mean value of all the pixels in the preset neighborhood with the corresponding pixel as the center. in
步骤S305:所述补偿值构成所述补偿矩阵图像。Step S305: The compensation value constitutes the compensation matrix image.
下面对本申请实施例提供的红外图像的校正装置进行介绍,下文描述的红外图像的校正装置与上文描述的红外图像的校正方法可相互对应参 照。The following describes the infrared image correction device provided by the embodiments of the present application. The infrared image correction device described below and the infrared image correction method described above can be referred to each other correspondingly.
图5为本申请实施例提供的红外图像的校正装置的结构框图,参照图5红外图像的校正装置可以包括:FIG. 5 is a structural block diagram of an apparatus for correcting an infrared image provided by an embodiment of the present application. Referring to FIG. 5, the apparatus for correcting an infrared image may include:
第一获取模块100,用于获取制冷红外成像系统在预设影响因素下采集的多帧图像,所述多帧图像中具有冷反射现象;The first acquisition module 100 is configured to acquire multiple frames of images collected by a refrigeration infrared imaging system under preset influencing factors, and the multiple frames of images have a cold reflection phenomenon;
第二获取模块200,用于获取所述制冷红外成像系统采集的待校正图像;The second acquisition module 200 is configured to acquire the to-be-corrected image collected by the refrigeration infrared imaging system;
判断模块300,用于判断所述待校正图像的画面状态,所述画面状态包括静止状态和运动状态;The judgment module 300 is used for judging the picture state of the to-be-corrected image, and the picture state includes a static state and a motion state;
校正模块400,用于根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。The correction module 400 is configured to correct the to-be-corrected image by using the multi-frame images according to the picture state to obtain a corrected image.
本实施例的红外图像的校正装置用于实现前述的红外图像的校正方法,因此红外图像的校正装置中的具体实施方式可见前文中的红外图像的校正方法的实施例部分,例如,第一获取模块100,第二获取模块200,判断模块300,校正模块400,分别用于实现上述红外图像的校正方法中步骤S101,S102,S103和S104,所以,其具体实施方式可以参照相应的各个部分实施例的描述,在此不再赘述。The infrared image correction apparatus of this embodiment is used to realize the aforementioned infrared image correction method, so the specific implementation of the infrared image correction apparatus can be found in the foregoing example section of the infrared image correction method, for example, the first acquisition The module 100, the second acquisition module 200, the judgment module 300, and the correction module 400 are respectively used to implement steps S101, S102, S103 and S104 in the above-mentioned infrared image correction method, so the specific implementation can be implemented with reference to the corresponding parts. The description of the example will not be repeated here.
本申请中的红外图像校正装置,通过获得具有冷反射现象的多帧图像和待校正的图像,确定待校正图像的画面状态是处于静止状态还是运动状态,进而根据不同的画面状态,利用具有冷反射现象的多帧图像对待校正图像进行校正,消除待校正图像中的冷反射现象,提升图像质量,并且无需改进制冷红外成像系统的光学设计,非常简单易行。The infrared image correction device in the present application determines whether the image state of the image to be corrected is in a static state or a moving state by obtaining multiple frames of images with cold reflection phenomenon and the image to be corrected, and then according to different The multi-frame image of the reflection phenomenon is used to correct the image to be corrected, eliminating the cold reflection phenomenon in the image to be corrected, improving the image quality, and without improving the optical design of the cooling infrared imaging system, which is very simple and easy to implement.
可选的,校正模块400包括:Optionally, the correction module 400 includes:
第一确定子模块,用于当所述画面状态为所述静止状态时,从所述多帧图像中选取与所述待校正图像的采集环境信息最接近的所述多帧图像,作为补偿矩阵图像;a first determination submodule, configured to select, from the multi-frame images, the multi-frame images that are closest to the collection environment information of the to-be-corrected image as a compensation matrix when the picture state is the static state image;
第二确定子模块,用于当所述画面状态为所述运动状态时,对所述多帧图像进行加权处理,得到模板冷像;对所述待校正图像进行场景抑制处理,得到估计冷像;根据所述模板冷像和所述估计冷像,确定所述补偿矩 阵图像;The second determination sub-module is configured to perform weighting processing on the multi-frame images when the picture state is the motion state to obtain a template cold image; perform scene suppression processing on the to-be-corrected image to obtain an estimated cold image ; According to the template cold image and the estimated cold image, determine the compensation matrix image;
校正子模块,用于根据所述补偿矩阵图像对所述待校正图像进行校正,得到所述校正后图像。A correction sub-module, configured to correct the to-be-corrected image according to the compensation matrix image to obtain the corrected image.
可选的,第二确定子模块包括:Optionally, the second determination submodule includes:
第一确定单元,用于根据所述待校正图像,确定背景像素值;a first determining unit, configured to determine a background pixel value according to the to-be-corrected image;
第一计算单元,用于计算所述待校正图像中每个像素点的像素值与所述背景像素值的差值绝对值,得到绝对值矩阵;a first calculation unit, used to calculate the absolute value of the difference between the pixel value of each pixel in the to-be-corrected image and the background pixel value to obtain an absolute value matrix;
第二确定单元,用于确定所述绝对值矩阵中每个所述差值绝对值的置信度,得到整体场景信息抑制的置信度矩阵;a second determining unit, configured to determine the confidence level of each absolute value of the difference in the absolute value matrix, and obtain a confidence level matrix for suppressing the overall scene information;
第三确定单元,用于确定所述待校正图像的低频信息和所述待校正图像中每个像素点的梯度信息;a third determining unit, configured to determine the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;
第四确定单元,用于根据所述低频信息和所述梯度信息确定局部场景信息抑制的低频分量;a fourth determining unit, configured to determine the low-frequency component suppressed by the local scene information according to the low-frequency information and the gradient information;
第五确定单元,用于根据所述置信度矩阵和所述低频分量确定场景抑制低频信息;a fifth determining unit, configured to determine scene suppression low-frequency information according to the confidence matrix and the low-frequency component;
第二计算单元,用于将所述场景抑制低频信息输入时域低通滤波算法公式,得到所述估计冷像。The second calculation unit is configured to input the scene suppression low-frequency information into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
可选的,第二确定单元包括:Optionally, the second determining unit includes:
第一判断子单元,用于判断所述差值绝对值是否小于第一预设阈值;a first judging subunit, configured to judge whether the absolute value of the difference is less than a first preset threshold;
第一确定子单元,用于若所述差值绝对值小于第一预设阈值,则确定所述差值绝对值的所述置信度为第一置信度;a first determination subunit, configured to determine the confidence level of the absolute value of the difference as a first confidence level if the absolute value of the difference is less than a first preset threshold;
第二判断子单元,用于若所述差值绝对值不小于第一预设阈值,则判断所述差值绝对值是否大于第二预设阈值;所述第二预设阈值大于所述第一预设阈值;A second judging subunit, configured to judge whether the absolute value of the difference is greater than a second preset threshold if the absolute value of the difference is not less than a first preset threshold; the second preset threshold is greater than the first preset threshold a preset threshold;
第二确定子单元,用于若所述差值绝对值大于所述第二预设阈值,则确定所述差值绝对值的所述置信度为第二置信度;所述第一置信度大于所述第二置信度;a second determination subunit, configured to determine the confidence level of the absolute value of the difference as the second confidence level if the absolute value of the difference is greater than the second preset threshold; the first confidence level is greater than the second confidence level;
第三确定子单元,用于若所述差值绝对值不大于所述第二预设阈值,则根据预设函数确定所述差值绝对值的所述置信度。A third determination subunit, configured to determine the confidence level of the absolute value of the difference according to a preset function if the absolute value of the difference is not greater than the second preset threshold.
可选的,第二确定子模块包括:Optionally, the second determination submodule includes:
第六确定单元,用于确定所述模板冷像和所述估计冷像在预设滑动窗口内中心像素点的相关系数,并得到相关系数矩阵;The sixth determination unit is used to determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel point in the preset sliding window, and obtain a correlation coefficient matrix;
判断单元,用于判断每个所述相关系数是否大于系数阈值;a judging unit for judging whether each of the correlation coefficients is greater than a coefficient threshold;
第七确定单元,用于若所述相关系数大于所述系数阈值,则在所述估计冷像中取对应像素点的像素值作为补偿值;a seventh determination unit, configured to take the pixel value of the corresponding pixel in the estimated cold image as a compensation value if the correlation coefficient is greater than the coefficient threshold;
第八确定单元,用于若所述相关系数不大于所述系数阈值,则在所述估计冷像中确定对应的对应像素点,并取所述对应像素点的预设邻域内的所有像素点的像素均值作为所述补偿值;an eighth determination unit, configured to determine the corresponding corresponding pixel in the estimated cold image if the correlation coefficient is not greater than the coefficient threshold, and take all the pixels in the preset neighborhood of the corresponding pixel The pixel mean value of is used as the compensation value;
构成单元,用于所述补偿值构成所述补偿矩阵图像。and a forming unit for forming the compensation matrix image with the compensation value.
可选的,第二确定子模块包括处理单元,所述处理单元具体用于对所述多帧图像进行平均加权处理,得到所述模板冷像。Optionally, the second determination sub-module includes a processing unit, and the processing unit is specifically configured to perform an average weighting process on the multiple frames of images to obtain the template cold image.
可选的,判断模块300具体用于利用帧差法或者定位法判断所述待校正图像的所述画面状态。Optionally, the judging module 300 is specifically configured to judge the picture state of the image to be corrected by using a frame difference method or a positioning method.
下面对本申请实施例提供的红外图像校正设备进行介绍,下文描述的红外图像校正设备与上文描述的红外图像的校正方法可相互对应参照。The infrared image correction device provided by the embodiments of the present application is introduced below, and the infrared image correction device described below and the infrared image correction method described above can be referred to each other correspondingly.
一种红外图像校正设备,包括:An infrared image correction device, comprising:
存储器,用于存储计算机程序;memory for storing computer programs;
处理器,用于执行所述计算机程序时实现上述任一种所述红外图像的校正方法的步骤。The processor is configured to implement the steps of any one of the above-mentioned infrared image correction methods when executing the computer program.
下面对本申请实施例提供的红外图像的校正系统进行介绍,下文描述的红外图像的校正系统与上文描述的红外图像的校正方法可相互对应参照。The following describes the infrared image correction system provided by the embodiments of the present application. The infrared image correction system described below and the infrared image correction method described above can be referred to each other correspondingly.
请见图6,图6为本申请实施例所提供的一种制冷红外成像系统的结构示意图。Please refer to FIG. 6 , which is a schematic structural diagram of a refrigeration infrared imaging system provided by an embodiment of the present application.
一种制冷红外成像系统,所述制冷红外成像系统包括上述所述的红外图像校正设备。A refrigerated infrared imaging system includes the above-mentioned infrared image correction device.
制冷红外成像系统还包括光学设备、制冷探测器、供电设备、控温设备、显示器、图像处理控制器,其中,红外图像校正设备与图像处理控制器相连。The refrigeration infrared imaging system further includes optical equipment, refrigeration detectors, power supply equipment, temperature control equipment, display, and image processing controller, wherein the infrared image correction equipment is connected with the image processing controller.
本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其它实施例的不同之处,各个实施例之间相同或相似部分互相参见即可。对于实施例公开的装置而言,由于其与实施例公开的方法相对应,所以描述的比较简单,相关之处参见方法部分说明即可。The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.
专业人员还可以进一步意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。Professionals may further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, in order to clearly illustrate the possibilities of hardware and software. Interchangeability, the above description has generally described the components and steps of each example in terms of functionality. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块,或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。The steps of a method or algorithm described in conjunction with the embodiments disclosed herein may be directly implemented in hardware, a software module executed by a processor, or a combination of the two. A software module can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.
以上对本申请所提供的红外图像的校正方法、装置、设备及制冷红外成像系统进行了详细介绍。本文中应用了具体个例对本申请的原理及实施方式进行了阐述,以上实施例的说明只是用于帮助理解本申请的方法及其核心思想。应当指出,对于本技术领域的普通技术人员来说,在不脱离本申请原理的前提下,还可以对本申请进行若干改进和修饰,这些改进和修饰也落入本申请权利要求的保护范围内。The infrared image correction method, device, equipment and refrigeration infrared imaging system provided by the present application are described in detail above. Specific examples are used herein to illustrate the principles and implementations of the present application, and the descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principles of the present application, several improvements and modifications can also be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.
Claims (10)
- 一种红外图像的校正方法,其特征在于,包括:A method for calibrating an infrared image, comprising:获取制冷红外成像系统采集的具有冷反射现象的多帧图像;Acquiring multiple frames of images with cold reflection phenomenon collected by the cooling infrared imaging system;获取所述制冷红外成像系统采集的待校正图像;acquiring the to-be-corrected image collected by the refrigeration infrared imaging system;判断所述待校正图像的画面状态;所述画面状态包括静止状态和运动状态;Judging the picture state of the image to be corrected; the picture state includes a static state and a motion state;根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。According to the picture state, the to-be-corrected image is corrected by using the multi-frame images to obtain a corrected image.
- 如权利要求1所述的红外图像的校正方法,其特征在于,所述根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像包括:The method for calibrating an infrared image according to claim 1, wherein, according to the picture state, using the multiple frames of images to correct the image to be corrected, and obtaining the corrected image comprises:当所述画面状态为所述静止状态时,从所述多帧图像中选取与所述待校正图像的采集环境信息最接近的所述多帧图像,作为补偿矩阵图像;When the picture state is the static state, selecting the multi-frame image from the multi-frame images that is closest to the collection environment information of the to-be-corrected image as a compensation matrix image;当所述画面状态为所述运动状态时,对所述多帧图像进行加权处理,得到模板冷像;对所述待校正图像进行场景抑制处理,得到估计冷像;根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像;When the picture state is the motion state, weighting processing is performed on the multi-frame images to obtain a template cold image; scene suppression processing is performed on the to-be-corrected image to obtain an estimated cold image; according to the template cold image and the estimated cold image, to determine the compensation matrix image;根据所述补偿矩阵图像对所述待校正图像进行校正,得到所述校正后图像。Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.
- 如权利要求2所述的红外图像的校正方法,其特征在于,所述对所述待校正图像进行场景抑制处理,得到估计冷像包括:The infrared image correction method according to claim 2, wherein the performing scene suppression processing on the to-be-corrected image to obtain an estimated cold image comprises:根据所述待校正图像,确定背景像素值;determining a background pixel value according to the to-be-corrected image;计算所述待校正图像中每个像素点的像素值与所述背景像素值的差值绝对值,得到绝对值矩阵;Calculate the absolute value of the difference between the pixel value of each pixel in the to-be-corrected image and the background pixel value to obtain an absolute value matrix;确定所述绝对值矩阵中每个所述差值绝对值的置信度,得到整体场景信息抑制的置信度矩阵;determining the confidence level of each absolute value of the difference in the absolute value matrix to obtain a confidence level matrix for suppressing the overall scene information;确定所述待校正图像的低频信息和所述待校正图像中每个像素点的梯度信息;determining the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;根据所述低频信息和所述梯度信息确定局部场景信息抑制的低频分量;Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information;根据所述置信度矩阵和所述低频分量确定场景抑制低频信息;determining the scene suppression low-frequency information according to the confidence matrix and the low-frequency component;将所述场景抑制低频信息输入时域低通滤波算法公式,得到所述估计冷像。The low-frequency information of the scene suppression is input into a time-domain low-pass filtering algorithm formula to obtain the estimated cold image.
- 如权利要求3所述的红外图像的校正方法,其特征在于,所述确定所述绝对值矩阵中每个所述差值绝对值的置信度,包括:The infrared image correction method according to claim 3, wherein the determining the confidence level of each absolute value of the difference in the absolute value matrix comprises:判断所述差值绝对值是否小于第一预设阈值;judging whether the absolute value of the difference is less than a first preset threshold;若所述差值绝对值小于第一预设阈值,则确定所述差值绝对值的所述置信度为第一置信度;If the absolute value of the difference is smaller than the first preset threshold, determining the confidence of the absolute value of the difference as the first confidence;若所述差值绝对值不小于第一预设阈值,则判断所述差值绝对值是否大于第二预设阈值;所述第二预设阈值大于所述第一预设阈值;If the absolute value of the difference is not less than the first preset threshold, determine whether the absolute value of the difference is greater than a second preset threshold; the second preset threshold is greater than the first preset threshold;若所述差值绝对值大于所述第二预设阈值,则确定所述差值绝对值的所述置信度为第二置信度;所述第一置信度大于所述第二置信度;If the absolute value of the difference is greater than the second preset threshold, the confidence level of the absolute value of the difference is determined to be the second confidence level; the first confidence level is greater than the second confidence level;若所述差值绝对值不大于所述第二预设阈值,则根据预设函数确定所述差值绝对值的所述置信度。If the absolute value of the difference is not greater than the second preset threshold, the confidence level of the absolute value of the difference is determined according to a preset function.
- 如权利要求2至4任一项所述的红外图像的校正方法,其特征在于,所述根据所述模板冷像和所述估计冷像,确定所述补偿矩阵图像包括:The infrared image correction method according to any one of claims 2 to 4, wherein the determining the compensation matrix image according to the template cold image and the estimated cold image comprises:确定所述模板冷像和所述估计冷像在预设滑动窗口内中心像素点的相关系数,并得到相关系数矩阵;Determine the correlation coefficient of the template cold image and the estimated cold image at the center pixel in the preset sliding window, and obtain a correlation coefficient matrix;判断每个所述相关系数是否大于系数阈值;Judging whether each of the correlation coefficients is greater than a coefficient threshold;若所述相关系数大于所述系数阈值,则在所述估计冷像中取对应像素点的像素值作为补偿值;If the correlation coefficient is greater than the coefficient threshold, taking the pixel value of the corresponding pixel in the estimated cold image as a compensation value;若所述相关系数不大于所述系数阈值,则在所述估计冷像中确定对应的对应像素点,并取所述对应像素点的预设邻域内的所有像素点的像素均值作为所述补偿值;If the correlation coefficient is not greater than the coefficient threshold, determine the corresponding corresponding pixel in the estimated cold image, and take the pixel mean value of all the pixels in the preset neighborhood of the corresponding pixel as the compensation value;所述补偿值构成所述补偿矩阵图像。The compensation values constitute the compensation matrix image.
- 如权利要求5所述的红外图像的校正方法,其特征在于,所述对所述多帧图像进行加权处理,得到模板冷像包括:The method for calibrating an infrared image according to claim 5, wherein the performing weighting processing on the multi-frame images to obtain a template cold image comprises:对所述多帧图像进行平均加权处理,得到所述模板冷像。Perform an average weighting process on the multiple frames of images to obtain the template cold image.
- 如权利要求6所述的红外图像的校正方法,其特征在于,所述判断所述待校正图像的画面状态包括:The infrared image correction method according to claim 6, wherein the judging the picture state of the to-be-corrected image comprises:利用帧差法或者定位法判断所述待校正图像的所述画面状态。The frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
- 一种红外图像的校正装置,其特征在于,包括:A device for correcting an infrared image, comprising:第一获取模块,用于获取制冷红外成像系统在预设影响因素下采集的多帧图像,所述多帧图像中具有冷反射现象;a first acquisition module, configured to acquire multiple frames of images collected by the cooling infrared imaging system under preset influencing factors, and the multiple frames of images have cold reflection phenomenon;第二获取模块,用于获取所述制冷红外成像系统采集的待校正图像;a second acquisition module, configured to acquire the to-be-corrected image collected by the refrigeration infrared imaging system;判断模块,用于判断所述待校正图像的画面状态,所述画面状态包括静止状态和运动状态;a judgment module for judging the picture state of the image to be corrected, where the picture state includes a static state and a motion state;校正模块,用于根据所述画面状态,利用所述多帧图像对所述待校正图像进行校正,得到校正后图像。A correction module, configured to correct the image to be corrected by using the multi-frame images according to the picture state to obtain a corrected image.
- 一种红外图像校正设备,其特征在于,包括:An infrared image correction device, characterized in that it includes:存储器,用于存储计算机程序;memory for storing computer programs;处理器,用于执行所述计算机程序时实现如权利要求1至7任一项所述红外图像的校正方法的步骤。The processor is configured to implement the steps of the infrared image correction method according to any one of claims 1 to 7 when executing the computer program.
- 一种制冷红外成像系统,其特征在于,所述制冷红外成像系统包括如权利要求9所述的红外图像校正设备。A refrigerated infrared imaging system, characterized in that the refrigerated infrared imaging system comprises the infrared image correction device according to claim 9 .
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