WO2022041356A1

WO2022041356A1 - Method, apparatus and device for correcting infrared image, and refrigeration infrared imaging system

Info

Publication number: WO2022041356A1
Application number: PCT/CN2020/116451
Authority: WO
Inventors: 刘心荷; 康萌萌
Original assignee: 烟台艾睿光电科技有限公司
Priority date: 2020-08-27
Filing date: 2020-09-21
Publication date: 2022-03-03
Also published as: CN111968066A; CN111968066B

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

Infrared image correction method, device, equipment and refrigeration infrared imaging system

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.

technical field

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.

Background technique

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.

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.

Therefore, how to solve the above technical problems should be the focus of those skilled in the art.

SUMMARY OF THE INVENTION

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.

In order to solve the above-mentioned technical problems, the present application provides a method for calibrating an infrared image, including:

Acquiring multiple frames of images with cold reflection phenomenon collected by the cooling infrared imaging system;

acquiring the to-be-corrected image collected by the refrigeration infrared imaging system;

Judging the picture state of the image to be corrected; the picture state includes a static state and a motion state;

According to the picture state, the to-be-corrected image is corrected by using the multi-frame images to obtain a corrected image.

Optionally, according to the picture state, using the multi-frame images to correct the image to be corrected, and obtaining the corrected image includes:

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;

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;

Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.

Optionally, performing scene suppression processing on the to-be-corrected image to obtain an estimated cold image includes:

determining a background pixel value according to the to-be-corrected image;

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;

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;

determining the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;

Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information;

determining the scene suppression low-frequency information according to the confidence matrix and the low-frequency component;

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.

Optionally, the determining the confidence level of each absolute value of the difference in the absolute value matrix includes:

judging whether the absolute value of the difference is less than a first preset threshold;

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;

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;

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;

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.

Optionally, determining the compensation matrix image according to the template cold image and the estimated cold image includes:

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;

Judging whether each of the correlation coefficients is greater than a coefficient threshold;

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;

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;

The compensation values constitute the compensation matrix image.

Optionally, performing weighting processing on the multi-frame images to obtain a cold template image includes:

Perform an average weighting process on the multiple frames of images to obtain the template cold image.

Optionally, 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:

memory for storing computer programs;

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 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.

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.

In addition, the present application also provides a correction device, equipment and refrigeration infrared imaging system with the above advantages.

Description of drawings

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.

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;

3 is a flowchart of obtaining an estimated cold image provided by an embodiment of the present application;

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.

detailed description

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.

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.

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:

Step S101 : acquiring multiple frames of images with cold reflection phenomenon collected by a cooling infrared imaging system.

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.

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.

Step S102 : acquiring the to-be-corrected image collected by the cooling infrared imaging system.

It should be pointed out that 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.

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.

Step S104: According to the picture state, correct the image to be corrected by using the multi-frame images to obtain a corrected image.

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.

Specifically, the calculation formula of the template cold image is as formula (1),

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.

Step S1043: Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.

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.

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.

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:

Step S201: Determine a background pixel value according to the to-be-corrected image.

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.

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.

Specifically, the formula for calculating the absolute value matrix is as formula (2),

D(n)=abs(II _b ) (2)

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.

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.

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. For example, 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. For example, 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. For example, 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. For example, 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.

Specifically, please refer to formula (3) for the preset function,

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.

Confidence is actually a piecewise mapping function, as shown in Equation (4). The piecewise mapping function diagram is shown in Figure 5.

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.

Step S204: Determine low-frequency information of the image to be corrected and gradient information of each pixel in the image to be corrected.

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.

Specifically, the gradient information calculation formula is as formula (5),

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.

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.

Step S205: Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information.

Specifically, the low-frequency component calculation formula is as in Equation (6),

In the formula, Low is the low-frequency component, I _low is the low-frequency information, and 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.

Specifically, the calculation formula of scene suppression low-frequency information is as formula (7),

X=Low×p (7)

In the formula, X is the scene suppression low-frequency information, Low is the low-frequency component, and 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.

Specifically, the time-domain low-pass filtering algorithm formula is as shown in Equation (8),

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)

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.

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.

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.

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:

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),

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.

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.

Step S302: Determine whether each of the correlation coefficients is greater than a coefficient threshold.

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.

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.

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.

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.

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

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. Referring to FIG. 5, 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.

Optionally, 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.

Optionally, 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.

Optionally, 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.

Optionally, 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;

and a forming unit for forming the compensation matrix image with the compensation value.

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.

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.

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:

memory for storing computer programs;

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.

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.

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.

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.

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.

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.

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

A method for calibrating an infrared image, comprising:

Acquiring multiple frames of images with cold reflection phenomenon collected by the cooling infrared imaging system;

acquiring the to-be-corrected image collected by the refrigeration infrared imaging system;

Judging the picture state of the image to be corrected; the picture state includes a static state and a motion state;

According to the picture state, the to-be-corrected image is corrected by using the multi-frame images to obtain a corrected image.
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:

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;

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;

Correct the image to be corrected according to the compensation matrix image to obtain the corrected image.
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:

determining a background pixel value according to the to-be-corrected image;

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;

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;

determining the low-frequency information of the image to be corrected and the gradient information of each pixel in the image to be corrected;

Determine the low frequency component suppressed by the local scene information according to the low frequency information and the gradient information;

determining the scene suppression low-frequency information according to the confidence matrix and the low-frequency component;

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 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:

judging whether the absolute value of the difference is less than a first preset threshold;

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;

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;

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;

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.
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:

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;

Judging whether each of the correlation coefficients is greater than a coefficient threshold;

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;

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;

The compensation values constitute the compensation matrix image.
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:

Perform an average weighting process on the multiple frames of images to obtain the template cold image.
The infrared image correction method according to claim 6, wherein the judging the picture state of the to-be-corrected image comprises:

The frame-difference method or the positioning method is used to determine the picture state of the image to be corrected.
A device for correcting an infrared image, 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.
An infrared image correction device, characterized in that it includes:

memory for storing computer programs;

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.
A refrigerated infrared imaging system, characterized in that the refrigerated infrared imaging system comprises the infrared image correction device according to claim 9 .