A point target infrared quantitative processing method based on diffusion model fitting

Abstract

The present application relates to the field of photoelectric technology, and particularly relates to a point target infrared quantitative processing method based on diffusion model fitting. The present application provides a point target infrared quantitative processing method based on diffusion model fitting, comprising: modeling a diffusion process of a point target by using a Gaussian formula to obtain a target formula; setting a random noise formula, and establishing a background radiation formula according to the random noise formula; fitting and solving the target formula by using measurement data and the background radiation formula to obtain a variable of the target formula; and obtaining a point target radiation intensity by characteristic inversion after substituting the variable into the target formula. The present application provides a point target infrared quantitative processing method based on diffusion model fitting, and improves the measurement accuracy of the point target.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optoelectronic technology, in particular to a point target infrared quantitative processing method based on a diffusion model fitting. BACKGROUND

[0002] In actual target range measurement, the flying target in the air is usually far away from the optical system. When the radiation characteristic measurement is performed, the target is generally about 1*10 3 km away from the optical system, and the image formed by the target passing through the optical system is generally a point target, that is, the actual image area of the target on the detector surface is smaller than that of one pixel of the detector. At this time, due to the diffraction of the optical system, the energy is diffused. For this case, when the traditional radiation characteristic measurement method is applied to perform the radiation characteristic measurement, the accuracy of the radiation characteristic measurement is affected.

[0003] Therefore, in view of the above problems, the present application provides a point target radiation characteristic measurement method based on a diffusion model fitting, which improves the measurement accuracy of the point target. SUMMARY

[0004] The embodiment of the present application provides a point target infrared quantitative processing method based on a diffusion model fitting, which improves the measurement accuracy of the point target.

[0005] The embodiment of the present application provides a point target infrared quantitative processing method based on a diffusion model fitting, which includes:

[0006] The diffusion process of the point target is modeled by using a Gaussian formula to obtain a target formula;

[0007] A random noise formula is set, and a background radiation formula is established according to the random noise formula;

[0008] The target formula is fitted and solved by using measurement data and the background radiation formula to obtain a variable of the target formula;

[0009] After the variable is substituted into the target formula, the point target radiation intensity is obtained by characteristic inversion.

[0010] In a possible design, the target formula is fitted and solved by using measurement data and the background radiation formula to obtain a variable of the target formula, including:

[0011] The measurement data, the background radiation formula and the target formula are substituted into a loss function, and the variable of the target formula is obtained under the limitation of the loss function.

[0012] In a possible design, the loss function is as follows:

[0013]

[0014] wherein, L 测 is the measurement data, L m is the target formula, L bg is the background radiation formula, Γ 目 is the target region, Γ 背 is the background region, and x, y are coordinate values.

[0015] In a possible design, the target region is obtained in the following manner:

[0016] The target region is obtained by using a target detection and tracking algorithm.

[0017] In a possible design, the background region is obtained in the following manner:

[0018] The background region is obtained by expanding pixels outside the target region.

[0019] In a possible design, the target formula is as follows:

[0020] L m (x,y)=α*exp(-(x-μ x ) 2 / σ x -(y-μ y ) 2 / σ y )

[0021] wherein, x, y are coordinate values, α is a coefficient of the formula, μ x and μ y are mean values of x and y respectively.

[0022] In a possible design, the background radiation formula is as follows:

[0023]

[0024] wherein, μ is a mean value of a uniform background, and n is random noise.

[0025] In a possible design, the measurement data is obtained in the following manner:

[0026] A calibration file is loaded, and a gray-scale image collected by a measuring device is converted into a radiation brightness image.

[0027] The radiation brightness image in the target region and the background region is taken as the measurement data.

[0028] Compared with the prior art, the present application has at least the following beneficial effects:

[0029] ​Firstly, a model is established for the diffusion process of a point target by using Gaussian formula to obtain a target formula. At this time, the variables of the target formula are not determined. A random noise formula is set, and a background radiation formula is established according to the random noise formula. After the background radiation formula and the target formula are superimposed, the total radiation brightness formula is obtained, and the measurement data obtained by the test equipment is the total brightness superimposed with the target brightness and the background radiation. The measurement data obtained is combined with the background radiation formula to fit the target formula, and the five variables of the target formula are obtained, that is, the exact target formula is obtained, and the radiation intensity of the point target can be obtained through characteristic inversion. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 is a flow chart of a point target infrared quantitative processing method based on diffusion model fitting provided by the embodiment of the present application;

[0032] Figure 2a is a target modeling schematic diagram provided by the related art of the embodiment of the present application;

[0033] Figure 2b is a background modeling schematic diagram provided by the related art of the embodiment of the present application;

[0034] Figure 2c is a superposition modeling schematic diagram provided by the related art of the embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0036] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance; unless otherwise specified or stated, the term "plurality" means two or more; the terms "connection", "fixation" and the like shall be interpreted broadly, for example, "connection" can be fixed connection, or detachable connection, or integrally connected, or electrically connected; it can be directly connected, or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0037] In the description of the present specification, it should be understood that the "upper", "lower" and the like described in the embodiments of the present application are described with the angle shown in the drawings, and should not be understood as limiting the embodiments of the present application. In addition, in the context, it should also be understood that when referring to one element connected to another element "on" or "below", it can be directly connected to another element "on" or "below", or indirectly connected to another element "on" or "below" through an intermediate element.

[0038] As described previously, when quantitatively measuring the radiation characteristics of a point target, the point target will have a diffraction effect after passing through the optical system of the measuring device, and after diffraction, it will be dispersed into a spot on the imaging target surface, and then superimposed on the background. Assuming that the energy distribution of the dispersed point target obeys a Gaussian distribution, as shown in Figure 2a To obtain accurate target energy, the entire Gaussian function needs to be integrated. Assuming that the background area is a uniform background, the background image is obtained after being collected by the measuring device. Due to the influence of the dark current of the imaging circuit (assuming that the dark current is equal to random noise), the obtained background image is as shown in Figure 2b The point target image obtained after superimposing the target on the background is as shown in Figure 2c

[0039] In order to obtain accurate calculation of the radiation characteristics of the target, a threshold segmentation method is usually used to segment the target area, and the segmentation threshold is generally set to the mean value of the background area plus 5 times the standard deviation of the background area, such as the red dashed line in the figure. After segmentation, the target area is D1-D2 area, and then the radiation energy of the target is inverted. The Gaussian function corresponding to the inverted radiation energy of the target is I1 area, and theoretically the radiation energy of the target is equal to I1+I2+I3. The target radiation energy calculated by the current conventional method is less than the theoretical target radiation energy, especially when the signal-to-noise ratio of the target is small, the error is larger. Therefore, it is necessary to study a new point target radiation characteristic data inversion algorithm.

[0040] In order to solve the above problems, please refer to Figure 1 The embodiments of the present application provide a point target infrared quantitative processing method based on dispersion model fitting, comprising:​

[0041] The diffusion process of the point target is modeled by a Gaussian formula to obtain a target formula;

[0042] A random noise formula is set, and a background radiation formula is established according to the random noise formula;

[0043] The target formula is fitted and solved by using the measurement data and the background radiation formula to obtain variables of the target formula;

[0044] After the variables are substituted into the target formula, the point target radiation intensity is obtained through characteristic inversion.

[0045] First, a model of the diffusion process of the point target is established by a Gaussian formula to obtain a target formula. At this time, the variables of the target formula are not determined. A random noise formula is set, and a background radiation formula is established according to the random noise formula. After the background radiation formula and the target formula are superimposed, the total radiation brightness formula is obtained, and the measurement data obtained by the test equipment is the total brightness superimposed with the target brightness and the background radiation. The measurement data obtained is combined with the background radiation formula to fit the target formula, and five variables of the target formula are obtained, that is, the exact target formula is obtained, and then the radiation intensity of the point target can be obtained through characteristic inversion.

[0046] In some embodiments of the present application, the target formula is fitted and solved by using the measurement data and the background radiation formula to obtain the variables of the target formula, including:

[0047] The measurement data, the background radiation formula and the target formula are substituted into a loss function, and the variables of the target formula are solved under the limitation of the loss function.

[0048] In some embodiments of the present application, the loss function is as follows:

[0049]

[0050] Wherein, L 测 is the measurement data, L m is the target formula, L bg is the background radiation formula, Γ 目 is the target region, Γ 背 is the background region, and x and y are coordinate values.

[0051] In some embodiments of the present application, the target region is obtained by the following method:

[0052] The target region is obtained by using a target detection and tracking algorithm.

[0053] In some embodiments of the present application, the background region is obtained by the following method:

[0054] The background region is obtained by expanding the pixels outside the target region.

[0055] In some embodiments of the present application, the target formula is as follows:

[0056] L m (x,y) = a * exp(-(x-μ x ) 2 / σ x -(y-μ y ) 2 / σ y )

[0057] wherein x and y are coordinate values, a is a coefficient of the formula, μ x and μ y are mean values of x and y respectively.

[0058] In some embodiments of the present application, the background radiation formula is as follows:

[0059]

[0060] wherein μ is a mean value of the uniform background, and n is random noise.

[0061] The total radiation brightness is L(x, y) = L m + L bg . The dark current of the measuring device and other factors introduce image noise.

[0062] In some embodiments of the present application, the measurement data is obtained by the following way:

[0063] loading the calibration file to convert the gray scale image collected by the measuring device into a radiation brightness image;

[0064] taking the radiation brightness image in the target region and the background region as the measurement data.

[0065] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.​

Claims

1. A point target infrared quantitative processing method based on diffusion model fitting, characterized in that, The method comprises the following steps: A target formula is obtained by modeling the diffusion process of a point target by Gauss formula; A random noise formula is set, and a background radiation formula is established according to the random noise formula; Variables of the target formula are obtained by fitting and solving the target formula by using measurement data and the background radiation formula; Point target radiation intensity is obtained by characteristic inversion after the variables are substituted into the target formula; Variables of the target formula are obtained by fitting and solving the target formula by using measurement data and the background radiation formula, comprising: The measurement data, the background radiation formula and the target formula are substituted into a loss function, and the variables of the target formula are solved under the limitation of the loss function; The loss function is as follows: wherein, is the measurement data, is the target formula, is the background radiation formula, is the target region, is the background region, x, y are coordinate values; The target formula is as follows: where x, y are coordinate values, a is a coefficient of the formula, μ x and μ y are the mean values of x and y, respectively.

2. The method of claim 1, wherein, The target region is obtained by the following method: The target region is obtained by using a target detection and tracking algorithm.

3. The method of claim 1, wherein, The background region is obtained by the following method: The background region is obtained by expanding pixels outside the target region.

4. The method of claim 1, wherein, The background radiation formula is as follows: where μ is the mean of the uniform background, is a random noise.

5. The method of claim 1, wherein, The measurement data are obtained by the following method: A calibration file is loaded, and a gray-scale image collected by a measurement device is converted into a radiation brightness image; The radiation brightness image in the target region and the background region is taken as the measurement data.

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