Image processing method, system and device and storage medium

Abstract

The invention discloses an image processing method, system and device and a storage medium, and relates to the technical field of image processing. The image processing method comprises the following steps: acquiring a detection map and geometric parameters of a passive Fourier infrared telemetry system; under the condition that the detection map contains the target substance, determining the equivalent approximate concentration of the target substance; wherein the equivalent general concentration is used for representing the concentration of a target substance in the passive Fourier infrared remote measurement system; determining a first gray value of the equivalent approximate concentration according to the equivalent approximate concentration and a preset mapping function; determining a first gray plane graph layer according to the first gray value and the geometric parameters; and according to the first gray plane graph layer, determining a target concentration situation diffusion trend graph of the target substance according to a preset interpolation algorithm. According to the method, the concentration situation diffusion trend chart of the target substance is determined according to the preset interpolation algorithm and the gray plane graph layer, and the concentration diffusion situation continuous distribution characteristics of the target substance are visually reflected.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an image processing method, system, device and storage medium. Background Art

[0002] In recent years, with advances in remote sensing technology and spectral analysis algorithms, passive Fourier transform infrared (FTIR) telemetry has shown great potential in areas such as gas leak detection, atmospheric pollution monitoring, and industrial safety. Existing passive FTIR telemetry systems typically scan the spectral information within the field of view to generate a two-dimensional map reflecting the distribution of substances. However, when processing complex scenes, existing technologies often produce discrete detection results due to the uneven distribution of substances within the field of view, making it difficult to intuitively reflect the continuous distribution characteristics of substances. Summary of the Invention

[0003] Purpose of the invention: The embodiments of the present application provide an image processing method, system, device and storage medium to intuitively reflect the continuous distribution characteristics of the concentration diffusion trend of the target substance.

[0004] Technical solution: An image processing method described in an embodiment of the present application,

[0005] Applied to a passive Fourier infrared telemetry system, the method comprises:

[0006] Obtaining a detection spectrum and geometric parameters of the passive Fourier infrared telemetry system;

[0007] When the detection spectrum contains the target substance, determining an equivalent approximate concentration of the target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier transform infrared telemetry system;

[0008] Determining a first grayscale value of the equivalent approximate concentration according to the equivalent approximate concentration and a preset mapping function;

[0009] Determine a first grayscale plane layer according to the first grayscale value and the geometric parameter;

[0010] According to the first grayscale plane layer, a target concentration situation diffusion trend map of the target substance is determined according to a preset interpolation algorithm.

[0011] In some embodiments, the geometric parameters include: optical path parameters, coordinate parameters, and motion state parameters; the detection spectrum includes a plurality of detection points of the target substance;

[0012] The method for determining the first grayscale plane layer includes:

[0013] Constructing a grayscale plane of the target substance according to the optical path parameters;

[0014] The first grayscale plane layer is determined according to the coordinate parameters, the motion state parameters, the first grayscale value of each detection point, and the grayscale plane.

[0015] In some embodiments, the image processing method further includes:

[0016] Divide the grayscale plane into m*n pixel blocks, where m and n are positive integers;

[0017] Constructing a rectangular coordinate system with the target vertex angle of the grayscale plane as the origin;

[0018] Define the coordinates of each detection point in the grayscale plane coordinate system.

[0019] In some embodiments, determining the first grayscale plane layer according to the coordinate parameters, the motion state parameters, the first grayscale value of each detected point, and the grayscale plane includes:

[0020] Determine the target coordinates of each detection point according to the coordinate parameters and the motion state parameters; wherein the target coordinates are used to represent the coordinates of the first grayscale value of each detection point in the grayscale plane;

[0021] The first grayscale plane layer is constructed according to the target coordinates of all detected points.

[0022] In some embodiments, determining the target concentration diffusion trend map of the target substance according to the first grayscale plane layer and a preset interpolation algorithm includes:

[0023] Expanding the first grayscale plane layer to obtain a second grayscale plane layer;

[0024] Determine a first mapped grayscale value of the second grayscale plane layer according to a preset bilinear interpolation algorithm;

[0025] determining a first neighborhood grayscale difference according to the first mapped grayscale value and a preset neighborhood grayscale difference model;

[0026] When the first neighborhood grayscale difference is greater than a preset grayscale difference value, determining a second neighborhood grayscale difference according to a preset neighboring interpolation algorithm and the first mapped grayscale value;

[0027] When the grayscale difference of the second neighborhood is greater than the preset grayscale difference value, return to repeatedly perform the operation of expanding the first grayscale plane layer, and determine the target concentration situation diffusion trend map when the grayscale difference of the second neighborhood is less than the preset grayscale difference value.

[0028] In some embodiments, the method for determining the second neighborhood grayscale difference includes:

[0029] When the grayscale difference of the first neighborhood is greater than a preset grayscale difference value, determining a second mapped grayscale value of the second grayscale plane layer according to a preset neighboring interpolation algorithm;

[0030] determining a second grayscale value according to the first mapped grayscale value and the second mapped grayscale value;

[0031] The second neighborhood grayscale difference is determined according to the second grayscale value and the preset neighborhood grayscale difference model.

[0032] In some embodiments, determining the target concentration situation diffusion trend map when the grayscale difference of the second neighborhood is less than the preset grayscale difference value includes:

[0033] The target grayscale value of the second grayscale plane layer is determined until the grayscale difference of the second neighborhood is less than the preset grayscale difference value, and the second grayscale plane layer is reduced to obtain the target concentration situation diffusion trend map.

[0034] In some embodiments, the calculation formula of the preset neighborhood grayscale difference model is:

[0035]

[0036] ΔI i =|I(x,y)-I(x′,y′)|;

[0037] Among them, S(x,y) is the neighborhood grayscale difference; AvgDiff(x,y) is the mean of the neighborhood grayscale difference; I max is the maximum grayscale value; N is the number of pixel blocks in the neighborhood; ΔI i is the grayscale difference of the i-th neighbor; I(x,y) is the grayscale value of a pixel block in the neighborhood; I(x′,y′) is the grayscale value of a neighbor pixel block in the neighborhood.

[0038] In some embodiments, the calculation formula of the preset mapping function is:

[0039]

[0040] Wherein, I is the first grayscale value, M is the upper limit of the first grayscale value, K is the mapping slope; and C is the equivalent approximate concentration.

[0041] In some embodiments, the method for determining the equivalent approximate concentration comprises:

[0042] The equivalent approximate concentration is determined by fitting a substance concentration library according to a preset quadratic term.

[0043] Accordingly, an embodiment of the present application further provides an image processing system, comprising:

[0044] An acquisition module is used to obtain the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system;

[0045] a first determining module, configured to determine an equivalent approximate concentration of the target substance when the detection spectrum contains the target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier transform infrared telemetry system;

[0046] A second determining module, configured to determine a first grayscale value of the equivalent approximate concentration according to the equivalent approximate concentration and a preset mapping function;

[0047] a third determining module, configured to determine a first grayscale plane layer according to the first grayscale value and the geometric parameter;

[0048] The fourth determining module is used to determine the target concentration situation diffusion trend map of the target substance according to the first grayscale plane layer and a preset interpolation algorithm.

[0049] Correspondingly, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the image processing method described above when executing the computer program.

[0050] Accordingly, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the image processing method as described above is implemented.

[0051] Beneficial effects: Compared with the prior art, the image processing method, system, device, and storage medium of the embodiments of the present application include: obtaining a detection spectrum and geometric parameters of a passive Fourier infrared telemetry system; determining an equivalent approximate concentration of the target substance when the detection spectrum contains a target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier infrared telemetry system; determining a first grayscale value of the equivalent approximate concentration based on the equivalent approximate concentration and a preset mapping function; determining a first grayscale plane layer based on the first grayscale value and the geometric parameters; and determining a target concentration situation diffusion trend map of the target substance according to a preset interpolation algorithm based on the first grayscale plane layer. The image processing method provided by the present application can determine the grayscale plane layer of the target substance by determining the grayscale value of the target substance in passive Fourier infrared telemetry detection, and determine the concentration situation diffusion trend map of the target substance according to the preset interpolation algorithm and the grayscale plane layer, thereby intuitively reflecting the continuous distribution characteristics of the concentration diffusion situation of the target substance according to the concentration situation diffusion trend map. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 is a flowchart of an image processing method provided in an embodiment of the present application;

[0054] Figure 2 is a schematic diagram of a detection spectrum provided in the examples of the present application;

[0055] Figure 3 is a grayscale plane coordinate diagram provided in an embodiment of the present application;

[0056] Figure 4 This is a bilinear interpolation effect diagram provided in an embodiment of the present application;

[0057] Figure 5 This is a neighboring interpolation effect diagram provided in an embodiment of the present application;

[0058] Figure 6 A target concentration diffusion trend diagram provided in an embodiment of the present application;

[0059] Figure 7 This is a schematic diagram of the overall process of an image processing method provided in an embodiment of the present application;

[0060] Figure 8 This is a block diagram of the principle structure of an image processing system provided in an embodiment of the present application;

[0061] Figure 9 It is a structural diagram of an electronic device provided in an embodiment of the present application.

[0062] Reference numerals:

[0063] 101 - acquisition module; 102 - first determination module; 103 - second determination module; 104 - third determination module; 105 - fourth determination module; 100 - image processing system. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0065] It should be understood that although the terms first, second, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below could be referred to as the second component without departing from the teachings of the present invention. As used herein, the term "and / or" includes any one and all combinations of one or more of the associated listed items.

[0066] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of exemplary embodiments and may not be to scale. The modules or processes in the drawings are not necessarily required to implement the present application and therefore cannot be used to limit the scope of protection of the present application.

[0067] The applicant's research has revealed that passive Fourier transform infrared (FTIR) remote sensing is a non-contact detection method widely used for environmental monitoring, chemical identification, and concentration estimation. By analyzing the infrared spectrum emitted within a target area and combining it with spectral data collected by a Fourier transform infrared spectrometer (FTIR), this technology can identify the characteristic spectra of specific chemicals and estimate their concentrations. In recent years, with advances in remote sensing technology and spectral analysis algorithms, passive FTIR remote sensing has demonstrated significant potential in areas such as gas leak detection, atmospheric pollution monitoring, and industrial safety.

[0068] In the passive Fourier infrared telemetry systems of related technologies, the spectral information within the field of view is usually scanned by the instrument to generate a two-dimensional map reflecting the distribution of the material. However, when dealing with complex scenes, related technologies often face the following challenges: First, the uneven distribution of materials in the field of view causes the detection results to be discrete, making it difficult to intuitively reflect the continuous distribution characteristics of the material; second, the lack of effective visualization methods makes it difficult to quickly interpret the detection results, especially in dynamic monitoring or large-scale scanning scenarios. In addition, when performing post-processing on the detection area, related technologies usually rely only on simple threshold segmentation or direct mapping, and fail to fully utilize spatial interpolation algorithms to optimize the visualization effect of the material distribution.

[0069] In recent years, image processing techniques such as interpolation, convolution, and filtering have been widely used for data smoothing and visualization enhancement. These techniques model the spatial relationships of discrete data, generating smooth transitions and providing a more intuitive representation of the spatial distribution of the data. However, in the field of passive Fourier transform infrared telemetry, few studies have combined such interpolation techniques with spectral measurements to optimize the visualization of material concentration distributions.

[0070] In view of this, the embodiments of the present application provide an image processing method, system, device and storage medium. The present application can determine the grayscale plane layer of the target substance by determining the grayscale value of the target substance in passive Fourier infrared telemetry detection, and determine the concentration situation diffusion trend map of the target substance according to the preset interpolation algorithm and the grayscale plane layer, thereby intuitively reflecting the continuous distribution characteristics of the concentration diffusion situation of the target substance according to the concentration situation diffusion trend map.

[0071] Figure 1 This is a flow chart of an image processing method provided in an embodiment of the present application. This method can be applied to a passive Fourier transform infrared telemetry system to intuitively reflect the concentration and diffusion status of a target substance. This method can be executed by an image processing system, which can be implemented using software and / or hardware and can be configured in a processor or controller of the passive Fourier transform infrared telemetry system.

[0072] See also Figure 1 , the method comprises the following steps:

[0073] Step 110: Acquire the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system.

[0074] Among them, the detection map is the two-dimensional map data obtained by detecting the target area through a passive Fourier infrared telemetry system.

[0075] Figure 2 Schematic diagram of a detection spectrum provided in the examples of the present application. Figure 2 The first grayscale plane layer is composed of all detection points. Different color blocks represent the equivalent approximate concentration of the detection points and the grayscale value after mapping. For example, in the technical solution of the embodiment of the present application, taking the gas leakage detection of device Q as an example, the detection map is the gas leakage detection map data of device Q, such as Figure 2 The image shown.

[0076] The geometric parameters include: optical path parameters, coordinate parameters and motion state parameters.

[0077] Step 120: If the detection spectrum contains the target substance, determine the equivalent approximate concentration of the target substance.

[0078] Among them, the equivalent approximate concentration is used to characterize the concentration of target substances in passive Fourier transform infrared telemetry systems.

[0079] The target substance may be sulfur dioxide or other substances, and may be set according to actual conditions, and is not specifically limited here.

[0080] The detection spectrum includes multiple detection points of the target substance. For example, the detection spectrum may include multiple target substances, such as target substance A1, target substance A2, target substance A3, etc. If target substance A1 is detected in multiple locations in the detection spectrum, that is, target substance A1 includes multiple detection points.

[0081] Specifically, a detection spectrum and geometric parameters of a passive Fourier infrared telemetry system are obtained. Based on the detection spectrum, it is determined whether the detection spectrum contains the target substance A1. If the detection spectrum contains the target substance A1, the equivalent approximate concentration of the target substance A1 is further determined.

[0082] In some embodiments, the method for determining the equivalent approximate concentration includes: determining the equivalent approximate concentration by fitting a substance concentration library according to a preset quadratic term.

[0083] The specific implementation process for determining the equivalent approximate concentration based on a preset quadratic fitting library is, for example, to collect detection spectra at different sample concentrations of a substance M, integrate the detection spectra, and fit a quadratic curve. For a detection spectrum p, the integral is calculated to obtain the corresponding equivalent approximate concentration.

[0084] Step 130 : Determine a first grayscale value of the equivalent approximate density according to the equivalent approximate density and a preset mapping function.

[0085] Specifically, the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system are obtained. Based on the detection spectrum, it is determined whether the detection spectrum contains target substance A1. If the detection spectrum contains target substance A1, the equivalent approximate concentration of target substance A1 is determined based on a preset quadratic term fitting substance concentration library. Then, based on the equivalent approximate concentration and a preset mapping function, a visual grayscale value of the equivalent approximate concentration of the target substance, i.e., a first grayscale value, is obtained, facilitating the subsequent generation of a concentration trend map of the target substance.

[0086] In some embodiments, the calculation formula of the preset mapping function is:

[0087]

[0088] Wherein, I is the first grayscale value, M is the upper limit of the first grayscale value, K is the mapping slope; and C is the equivalent approximate concentration.

[0089] The value range of M is [0, 255]. The value range of k is (0, 1.5). The value range of the equivalent approximate concentration is (0, +∞), and the value range of the first grayscale value is (0, 255).

[0090] Specifically, a detection spectrum and geometric parameters of a passive Fourier infrared telemetry system are obtained. Based on the detection spectrum, it is determined whether the detection spectrum contains target substance A1. If target substance A1 is present in the detection spectrum, the equivalent approximate concentration of target substance A1 is determined based on a preset quadratic term fitted substance concentration library. Then, by substituting the equivalent approximate concentration into the calculation formula of a preset mapping function, a visual grayscale value of the equivalent approximate concentration of the target substance, i.e., a first grayscale value I, is calculated.

[0091] Step 140: Determine a first grayscale plane layer according to the first grayscale value and the geometric parameters.

[0092] The geometric parameters include: optical path parameters, coordinate parameters and motion state parameters.

[0093] Specifically, the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system are obtained. Based on the detection spectrum, it is determined whether the detection spectrum contains the target substance A1. If the detection spectrum contains the target substance A1, the equivalent approximate concentration of the target substance A1 is determined based on the preset quadratic term fitting material concentration library. Then, by substituting the equivalent approximate concentration into the calculation formula of the preset mapping function, a visual grayscale value of the equivalent approximate concentration of the target substance, i.e., a first grayscale value I, can be calculated. Finally, a first grayscale plane layer is determined based on the first grayscale value and the geometric parameters, so that a concentration situation diffusion trend map of the target substance can be further obtained based on the first grayscale plane layer.

[0094] In some embodiments, the method for determining the first grayscale plane layer includes the following steps:

[0095] Step 1: Construct the grayscale plane of the target material according to the optical path parameters.

[0096] The optical path parameters are optical path parameters of the passive Fourier infrared telemetry system itself. Exemplarily, the optical path parameters include an incident angle.

[0097] In some embodiments, the specific implementation process of constructing the grayscale plane of the target substance at each detection point based on the optical path parameters is as follows: in the system, the detection sensor and the visible camera can be considered to be at the same point. The actual field of view angle of the visible camera is α*β, and the detection optical path can be equivalent to a cone with a vertex angle of γ. Then the ratio of the horizontal field of view angle and the vertical field of view angle of the detection optical path to the visible camera is fixed ( and ), in order to simplify the calculation, the projection of the detection light path at a certain distance on the visible camera field of view plane is equivalent to a square (the ratio of the side length to the camera horizontal field of view angle is ). The camera field of view is equivalent to a grayscale plane of w*h pixels, so the projection of the detection light path on the grayscale plane is a square with a side length of a pixels, where

[0098] The visible field of view is mapped to a grayscale plane of size w*h pixels. According to the system optical path parameters, assuming that the system incident angle is γ, the actual field of view angle of the visible camera is α*β, and the detection point can be equivalent to a square with a side length of a pixel, where Assume that the grayscale plane can be divided into m*n squares. The edges that do not form squares are replaced with rectangles. That is, the length of the grayscale plane is ma and the width is na.

[0099] Step 2: Determine a first grayscale plane layer according to the coordinate parameters, the motion state parameters, the first grayscale value of each detection point, and the grayscale plane.

[0100] The coordinate parameters are the coordinate system of the passive Fourier infrared telemetry system itself, and the motion state parameters are the real-time motion state of the passive Fourier infrared telemetry system.

[0101] In some embodiments, a first grayscale plane layer is determined based on coordinate parameters, motion state parameters, the first grayscale value of each detection point, and the grayscale plane, including: determining the target coordinates of each detection point based on the coordinate parameters and the motion state parameters; wherein the target coordinates are used to represent the coordinates of the first grayscale value of each detection point in the grayscale plane; and constructing a first grayscale plane layer based on the target coordinates of all detection points.

[0102] Specifically, a grayscale plane of the target substance is constructed based on the optical path parameters. Then, based on the coordinate parameters and motion state parameters, the target coordinates of the first grayscale value of each detection point in the grayscale plane are determined, for example, the coordinates (x, y) of detection point P. This method can be used to obtain the target coordinates of the first grayscale value of all detection points in the detection spectrum of the target substance in the grayscale plane. Thus, a first grayscale plane layer can be obtained based on the target coordinates of all detection points.

[0103] Figure 3 is a grayscale plane coordinate diagram provided in an embodiment of the present application. In some embodiments, the image processing method further includes: dividing the grayscale plane into m*n pixel blocks, where m and n are positive integers; constructing a rectangular coordinate system with the target vertex of the grayscale plane as the origin; and defining the coordinates of each detected point in the grayscale plane coordinate system.

[0104] Wherein, the pixel block is a square block. Wherein, the target vertex is one of the vertex angles of the gray plane. For example, in the technical solution of the embodiment of the present application, Figure 3 The rectangular coordinate system is constructed by taking the upper left corner of the grayscale plane shown as the origin as an example for explanation.

[0105] For example, see Figure 3Initialize the grayscale plane to a visible field of view size of w*h. Based on the optical path parameters of the passive Fourier infrared telemetry system, assume an incident angle of γ and a visible field of view of α*β. The detection area is equivalent to a square with a side length of a. Assume that the grayscale plane can be divided into m*n squares. Rectangles are used to replace edges that do not form squares. Therefore, the length of the grayscale plane is ma and the width is na.

[0106] Then, a rectangular coordinate system is established with the upper left corner of the grayscale plane as the origin, with the positive direction of the x-axis pointing downward and the positive direction of the y-axis pointing rightward. Secondly, the coordinates (x, y) of any detection point P are defined as the number of a's contained in the lengths of the upper left corner of the grayscale plane from the y-axis and the x-axis. That is, if the coordinates of P are (x, y), then the lengths from the y-axis to the x-axis are xa and ya, respectively. The grayscale value of P is defined as f(P) = f(x, y). Here, f(x, y) is a known binary function, which is the two-dimensional matrix obtained by step 130 above.

[0107] The specific process for obtaining the target coordinates of the first grayscale value of all detection points of the target substance in the detection spectrum on the grayscale plane is as follows: obtaining the motion posture parameters of the passive Fourier infrared telemetry system, that is, determining the initial detection coordinates of the passive Fourier infrared telemetry system, and obtaining and saving the system's real-time coordinates P, real-time coordinates T, and real-time coordinates Z in real time. Then, based on the motion posture parameters, the coordinates (x, y) of all detection points are determined. That is, the coordinates of all detection points on the grayscale plane are calculated by combining the detection point time, the system's real-time coordinates, and the system's initial detection coordinates.

[0108] Specifically, determine the initial coordinates of the system detection, obtain the real-time coordinates P and T of the system in real time and save them. Combined with the real-time coordinates of the system at the detection point, the system detects the initial coordinates P0 and T0, and calculates the coordinates of all detection points in the grayscale plane. The coordinates calculated in the grayscale plane are Then, through the above segmented plane, calculate which segmented area it belongs to, and thus determine the coordinates on the grayscale plane.

[0109] Step 150: Determine a target concentration diffusion trend diagram of the target substance according to a preset interpolation algorithm based on the first grayscale plane layer.

[0110] The preset interpolation algorithms include multiple interpolation algorithms, such as a bilinear interpolation algorithm and a nearest neighbor interpolation algorithm.

[0111] Specifically, the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system are obtained. It is determined whether the detection spectrum contains the target substance A1 based on the detection spectrum. If the detection spectrum contains the target substance A1, the equivalent approximate concentration of the target substance A1 is determined based on the preset quadratic fitting material concentration library. Then, by substituting the equivalent approximate concentration into the calculation formula of the preset mapping function, the visual grayscale value of the equivalent approximate concentration of the target substance, that is, the first grayscale value I, can be calculated. Secondly, the grayscale plane of the target substance is constructed according to the optical path parameters, and the first grayscale plane layer is determined according to the coordinate parameters, the motion state parameters, the first grayscale value of each detection point and the grayscale plane. Finally, based on the first grayscale plane layer, the target concentration situation diffusion trend map of the target substance is determined according to the preset interpolation algorithm, thereby intuitively reflecting the concentration diffusion situation distribution characteristics of the target substance through the target concentration situation diffusion trend map.

[0112] In some embodiments, determining a target concentration diffusion trend map of a target substance according to a preset interpolation algorithm based on the first grayscale plane layer includes the following steps:

[0113] Step 1: Expand the first grayscale plane layer to obtain a second grayscale plane layer.

[0114] The first grayscale plane layer is expanded k times. K is equal to 2, 3, 4, 5, etc., which can be optimized and adjusted according to actual conditions and is not specifically limited here. Specifically, the length and width of the first grayscale plane layer are expanded to k times the original, that is, the length of the plane is km*a, the width is kn*a, and the area is k 2 *m*n*a 2 .

[0115] Step 2: Determine the first mapped grayscale value of the second grayscale plane layer according to a preset bilinear interpolation algorithm.

[0116] Specifically, on the first grayscale plane layer, the first mapping grayscale value F(x,y) of the expanded second grayscale plane layer is obtained through the constrained preset bilinear interpolation algorithm. The specific process is as follows: First, let the coordinates of the point P' corresponding to the detected point P in the enlarged image be (x',y'), and the coordinate mapping relationship between point P' and point P is:

[0117]

[0118] Then, define the gray value F(P′) of point P′ as the independent variable transformation of the gray value f(P) of point P:

[0119] F(P′)=f(P);

[0120] Select a rectangle with 4 points Q on the original grayscale plane (i.e. the first grayscale plane layer) 11(x1,y1),Q 12 (x1,y2),Q 21 (x2,y1),Q 22 (x2,y2). Among them, x1 <x<x2,y1<y<y2。

[0121] Next, on y=y1, assume that points R1 and Q 11 , Q 21 The gray value satisfies the linear change condition, that is:

[0122]

[0123] Get the grayscale value of point R1:

[0124]

[0125] Among them, f(Q 11 )、f(Q 21 ) are points Q 11 , Q 21 Gray value.

[0126] On y=y2, assume that points R2 and Q 12 , Q 22 The gray value satisfies the linear change condition, that is:

[0127]

[0128] Get the grayscale value of point R2:

[0129]

[0130] Among them, f(Q 12 )、f(Q 22 ) are points Q 12 , Q 22 Gray value.

[0131] At x=x, it is assumed that the grayscale values ​​of points P, R1, and R2 satisfy the linear change condition, that is:

[0132]

[0133] Get the grayscale value of point P:

[0134]

[0135] Substituting f(R1) and f(R2) into the equation, we can simplify:

[0136]

[0137] To simplify the calculation, we take 4 adjacent pixel blocks.

[0138] Among them, the relationship between x1, x2, y1, and y2 is as follows:

[0139] x2=x1+1;

[0140] y2=y1+1;

[0141] f(P) can be simplified as:

[0142] f(P)=f(x,y)=f(Q 11 )(x2-x)(y2-y)+f(Q 21 )(x-x1)(y2-y)+

[0143] f(Q 12 )(x2-x)(y-y1)+f(Q 22 )(x-x1)(y-y1)];

[0144] Among them, x and y may take decimals, let:

[0145]

[0146] Among them, i is the integer part, u is the decimal part, j is the integer part, and v is the decimal part.

[0147] F(P′) can be simplified to:

[0148] F(P′)=f(P)=[f(Q 11 )(1-u)(1-v)+f(Q 21 )(1-u)*v+f(Q 12 )*u*(1-v)+f(Q 22 )*u*v];

[0149] Among them, f(Q 11 )、f(Q 21 )、f(Q 12 )、f(Q 22 ) can be simplified to:

[0150] f(Q 11 )=f(i,j);

[0151] f(Q 21 )=f(i+1,j);

[0152] f(Q 12 )=f(i,j+1);

[0153] f(Q 22 )=f(i+1,j+1);

[0154] Then F(P′) can be simplified as:

[0155] F(P′)=f(P)=[f(i,j)(1-u)(1-v)+f(i+1,j)(1-u)*v+f(i,j+1)*u*(1-v)+f(i+1,j+1)*u*v];

[0156] Therefore, the first mapping grayscale value can be obtained according to the above calculation formulas. Figure 4 is a bilinear interpolation effect diagram provided in the embodiment of this application. Figure 2 The detection spectrum shown is obtained after processing according to the above preset bilinear interpolation algorithm Figure 4 The effect shown.

[0157] Step 3: Determine a first neighborhood grayscale difference based on the first mapped grayscale value and a preset neighborhood grayscale difference model.

[0158] Specifically, the first mapped grayscale value is substituted into a preset domain grayscale difference model calculation formula to calculate the first domain grayscale difference.

[0159] In some embodiments, the calculation formula of the preset neighborhood grayscale difference model is:

[0160]

[0161] ΔI i =|I(x,y)-I(x′,y′)|;

[0162] Among them, S(x,y) is the neighborhood grayscale difference; AvgDiff(x,y) is the mean of the neighborhood grayscale difference; I max is the maximum grayscale value; N is the number of pixel blocks in the neighborhood; ΔI i is the grayscale difference of the i-th neighbor; I(x,y) is the grayscale value of a pixel block in the neighborhood; I(x′,y′) is the grayscale value of a neighbor pixel block in the neighborhood.

[0163] Among them, I max The maximum grayscale value is 255. Wherein, N is the number of squares in the neighborhood. For example, in the embodiment of the present application, 8 neighborhoods are selected, that is, N=8.

[0164] In the process of calculating the first neighborhood grayscale difference according to the calculation formula of the preset neighborhood grayscale difference model, the grayscale value in the calculation formula of the preset neighborhood grayscale difference model is the first mapped grayscale value.

[0165] Step 4: When the first neighborhood grayscale difference is greater than a preset grayscale difference value, determine a second neighborhood grayscale difference according to a preset neighborhood interpolation algorithm and the first mapped grayscale value.

[0166] The preset grayscale difference value is a threshold value σ1, and its specific value can be set according to actual conditions and is not specifically limited here.

[0167] Specifically, after calculating a first neighborhood grayscale difference based on the first mapped grayscale value and a preset neighborhood grayscale difference model, a determination is made as to whether the first neighborhood grayscale difference is greater than a preset grayscale difference value. If the first neighborhood grayscale difference is greater than the preset grayscale difference value, a second neighborhood grayscale difference is determined based on a preset neighboring interpolation algorithm and the first mapped grayscale value.

[0168] In some embodiments, a method for determining a second neighborhood grayscale difference includes: determining a second mapping grayscale value of a second grayscale plane layer according to a preset neighborhood interpolation algorithm when the first neighborhood grayscale difference is greater than a preset grayscale difference value; determining a second grayscale value according to the first mapping grayscale value and the second mapping grayscale value; and determining the second neighborhood grayscale difference according to the second grayscale value and a preset neighborhood grayscale difference model.

[0169] Specifically, the process of determining the second mapped grayscale value of the second grayscale plane layer according to the preset neighbor interpolation algorithm is as follows: the grayscale value G(p′) of the point P′ (i.e., the second mapped grayscale value) is defined as the independent variable transformation of the grayscale value f(P) of the point P:

[0170] G(p′)=f(P);

[0171] Where [x] is defined to represent the rounding of x, and the calculation method of G(P′) is defined as:

[0172] G(P′)=f([x],[y]);

[0173] Here, G(P′) represents the mapping of point P′ to the point in the original image whose coordinates are integers closest to point P.

[0174] Among them, the four adjacent points of point P' are also Q 11 (x1,y1),Q 12 (x1,y2),Q 21 (x2,y1),Q 22 (x2,y2). Calculate the standard deviation σ of the grayscale values ​​of the selected 4 adjacent points:

[0175]

[0176] in, Defined as:

[0177]

[0178] Specifically, the process of determining the second grayscale value based on the first mapping grayscale value and the second mapping grayscale value is: weighting the first mapping grayscale value F(P′) and the second mapping grayscale value G(P′) to obtain W(P′), and defining W(P′) as the grayscale value of point P′.

[0179] Where W(P′) is defined as:

[0180] W(P′)=ω1*F(P′)+ω2*G(P′);

[0181] Among them, ω1 and ω2 are defined as:

[0182]

[0183] ω2=1-ω1;

[0184] Wherein, C is an adjustable parameter, and C ranges from 0.1 to 0.5. The specific value can be set according to the actual situation and is not specifically limited here.

[0185] Specifically, the process of determining the second neighborhood grayscale difference according to the second grayscale value and the preset neighborhood grayscale difference model is: substituting the second grayscale value into the preset neighborhood grayscale difference model calculation formula to calculate the second neighborhood grayscale difference.

[0186] In the process of calculating the second neighborhood grayscale difference according to the calculation formula of the preset neighborhood grayscale difference model, the grayscale value in the calculation formula of the preset neighborhood grayscale difference model is the second grayscale value. Figure 5 is a neighboring interpolation effect diagram provided in an embodiment of the present application. For example, Figure 4 The bilinear interpolation effect diagram shown is obtained after processing according to the above preset neighbor interpolation algorithm. Figure 5 The effect shown.

[0187] Step 5. When the grayscale difference in the second neighborhood is greater than the preset grayscale difference value, return to repeat the operation of expanding the first grayscale plane layer, and determine the target concentration situation diffusion trend map when the grayscale difference in the second neighborhood is less than the preset grayscale difference value.

[0188] Specifically, the first neighborhood grayscale difference is calculated based on the first mapping grayscale value and the preset neighborhood grayscale difference model. Then, it is determined whether the first neighborhood grayscale difference is greater than the preset grayscale difference. If the first neighborhood grayscale difference is greater than the preset grayscale difference, the second mapping grayscale value of the second grayscale plane layer is determined according to the preset neighborhood interpolation algorithm; the second grayscale value is determined according to the first mapping grayscale value and the second mapping grayscale value; the second neighborhood grayscale difference is determined according to the second grayscale value and the preset neighborhood grayscale difference model. Then, it is determined whether the second neighborhood grayscale difference is less than the preset grayscale difference. If the second neighborhood grayscale difference is greater than the preset grayscale difference, the operation of expanding the first grayscale plane layer is returned to be repeated, and the target concentration situation diffusion trend map is determined when the second neighborhood grayscale difference is less than the preset grayscale difference. Therefore, the concentration diffusion situation continuous distribution characteristics of the target substance can be intuitively reflected according to the target concentration situation diffusion trend map.

[0189] It should be noted that both the first neighborhood grayscale difference and the second neighborhood grayscale difference need to be normalized. When comparing the neighborhood grayscale difference with the preset grayscale difference value, the normalized neighborhood grayscale difference is compared therewith.

[0190] In some embodiments, a target concentration situation diffusion trend map is determined when the grayscale difference of the second neighborhood is less than a preset grayscale difference value, including: determining the target grayscale value of the second grayscale plane layer until the grayscale difference of the second neighborhood reaches the preset grayscale difference value, and reducing the second grayscale plane layer to obtain the target concentration situation diffusion trend map.

[0191] The operation of reducing the second grayscale plane layer may be: reducing the second grayscale plane layer to the first grayscale plane layer with a preset reduction factor. The preset reduction factor is:

[0192]

[0193] Specifically, the length and width of the grayscale plane are expanded to k times of the original, that is, the length of the plane is km*a, the width is kn*a, and the area is k 2 *m*n*a 2 . After processing, it is reduced to 1 / k.

[0194] Specifically, calculate again whether the normalized neighborhood grayscale difference (i.e., the second neighborhood grayscale difference) is less than the threshold σ1. If it is less than σ1, stop the calculation, obtain the grayscale value W(P′) of all points in the enlarged image, reduce the side length of the expanded grayscale plane to a preset reduction factor, and obtain the processed concentration plane (i.e., the target concentration situation diffusion trend map). If it is greater than σ1, repeat the above steps 1 to 5 N times until the desired effect is achieved. Among them, the value of N is equal to 2, 3, 4, etc., and can be optimized and adjusted according to actual conditions, and no specific limitation is made here.

[0195] Figure 6 This is a target concentration diffusion trend diagram provided in the embodiment of the present application. For example, according to the above image processing steps provided in the embodiment of the present application, the following can be obtained: Figure 6 The figure shows a continuous distribution characteristic diagram of the concentration diffusion of the target substance A1 in the gas leakage detection data of the device Q. The detection point is obtained to uniformly and continuously diffuse to the surrounding area according to the approximate concentration.

[0196] It can be understood that the image processing method provided in the present application can determine the grayscale plane layer of the target substance by determining the grayscale value of the target substance in passive Fourier infrared telemetry detection, and determine the concentration situation diffusion trend diagram of the target substance according to the preset interpolation algorithm and the grayscale plane layer, thereby intuitively reflecting the continuous distribution characteristics of the concentration diffusion situation of the target substance according to the concentration situation diffusion trend diagram.

[0197] Figure 7 This is a schematic diagram of the overall flow of an image processing method provided in an embodiment of the present application. Figure 7 The overall implementation process of the image processing method is as follows: divide the original image into blocks (i.e., the first grayscale plane layer). Then establish a coordinate system and define the coordinates of the detection point. Enlarge the length and width of the original image, and enlarge them k times respectively to obtain the second grayscale plane layer. Calculate the coordinates of any point P′ in the enlarged image. Define the first mapping grayscale value F(P′) and use multiple interpolation (for example, cubic interpolation) to calculate F(P′). Define the second mapping grayscale value G(P′) and use single interpolation to calculate G(P′). Then, calculate the standard deviation of the grayscale values ​​(i.e., RGB values) of the four adjacent points. Calculate the RGB value W(P′) of point P′. Reduce the pixel block to 1 / k of the original. Finally, determine whether the program has been executed N times. If so, obtain the image edge blur result (i.e., the target concentration situation diffusion trend map). If not, return to repeatedly execute the operation of dividing the original image into blocks. In this way, the target concentration situation diffusion trend map of the target substance can be obtained to intuitively reflect the continuous distribution characteristics of the concentration diffusion situation of the target substance.

[0198] It can be seen from this that the image processing method provided in the embodiment of the present application is a method for processing the approximate concentration diffusion situation based on passive Fourier infrared telemetry. This method establishes a two-dimensional mapping matrix of detection concentration-visual graphics in a fixed field of view by performing qualitative and quantitative analysis on the detection spectrum of the passive Fourier infrared telemetry system, and finally generates a continuous distribution map of the approximate concentration diffusion situation based on passive Fourier infrared telemetry through a variety of interpolation weighted processing, so as to enhance the intuitiveness and application value of the detection results. The image processing method provided in the present application can combine the spectral analysis results with the spatial interpolation technology in the passive Fourier infrared telemetry detection, and generate a continuous distribution effect that gradually diffuses from the high concentration center to the edge by visually dyeing and interpolating the detection area, so as to enhance the intuitiveness and application value of the detection results.

[0199] Figure 8 This is a principle structure diagram of an image processing system provided in the embodiment of the present application. Correspondingly, the embodiment of the present application also provides an image processing system, please refer to Figure 8 The image processing system 100 includes: an acquisition module 101, which is used to acquire a detection spectrum and geometric parameters of a passive Fourier infrared telemetry system; a first determination module 102, which is used to determine the equivalent approximate concentration of the target substance when the detection spectrum contains the target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier infrared telemetry system; a second determination module 103, which is used to determine a first grayscale value of the equivalent approximate concentration based on the equivalent approximate concentration and a preset mapping function; a third determination module 104, which is used to determine a first grayscale plane layer based on the first grayscale value and the geometric parameters; and a fourth determination module 105, which is used to determine a target concentration situation diffusion trend map of the target substance according to a preset interpolation algorithm based on the first grayscale plane layer.

[0200] The technical solution of the embodiment of the present application provides an image processing system that can determine the grayscale plane layer of the target substance by determining the grayscale value of the target substance in passive Fourier infrared telemetry detection, and determine the concentration situation diffusion trend diagram of the target substance according to a preset interpolation algorithm and the grayscale plane layer, thereby intuitively reflecting the continuous distribution characteristics of the concentration diffusion situation of the target substance according to the concentration situation diffusion trend diagram.

[0201] In some embodiments, the geometric parameters include: optical path parameters, coordinate parameters and motion state parameters; the detection spectrum includes multiple detection points of the target substance; the third determination module 104 is also used to: construct a grayscale plane of the target substance based on the optical path parameters; determine the first grayscale plane layer based on the coordinate parameters, motion state parameters, the first grayscale value of each detection point and the grayscale plane.

[0202] In some embodiments, the image processing method further includes:

[0203] Divide the grayscale plane into m*n pixel blocks, where m and n are positive integers;

[0204] Constructing a rectangular coordinate system with the target vertex angle of the grayscale plane as the origin;

[0205] Define the coordinates of each detection point in the grayscale plane coordinate system.

[0206] In some embodiments, the third determination module 104 is further configured to: determine the target coordinates of each detection point based on the coordinate parameters and the motion state parameters; wherein the target coordinates are used to represent the coordinates of the first grayscale value of each detection point in the grayscale plane;

[0207] Construct the first grayscale plane layer according to the target coordinates of all detected points.

[0208] In some embodiments, the fourth determination module 105 is also used to: expand the first grayscale plane layer to obtain a second grayscale plane layer; determine the first mapping grayscale value of the second grayscale plane layer according to a preset bilinear interpolation algorithm; determine the first neighborhood grayscale difference according to the first mapping grayscale value and a preset neighborhood grayscale difference model; when the first neighborhood grayscale difference is greater than the preset grayscale difference value, determine the second neighborhood grayscale difference according to the preset neighborhood interpolation algorithm and the first mapping grayscale value; when the second neighborhood grayscale difference is greater than the preset grayscale difference value, return to repeatedly execute the operation of expanding the first grayscale plane layer, and determine the target concentration situation diffusion trend map when the second neighborhood grayscale difference is less than the preset grayscale difference value.

[0209] In some embodiments, the fourth determination module 105 is also used to: determine the second mapping grayscale value of the second grayscale plane layer according to a preset neighboring interpolation algorithm when the first neighborhood grayscale difference is greater than a preset grayscale difference value; determine the second grayscale value according to the first mapping grayscale value and the second mapping grayscale value; determine the second neighborhood grayscale difference according to the second grayscale value and a preset neighborhood grayscale difference model.

[0210] In some embodiments, the fourth determination module 105 is also used to: determine the target grayscale value of the second grayscale plane layer until the grayscale difference of the second neighborhood is less than the preset grayscale difference value, and reduce the second grayscale plane layer to obtain a target concentration situation diffusion trend map.

[0211] In some embodiments, the calculation formula of the preset neighborhood grayscale difference model is:

[0212]

[0213] ΔI i =|I(x,y)-I(x′,y′)|;

[0214] Among them, S(x,y) is the neighborhood grayscale difference; AvgDiff(x,y) is the mean of the neighborhood grayscale difference; I max is the maximum grayscale value; N is the number of pixel blocks in the neighborhood; ΔI i is the grayscale difference of the i-th neighbor; I(x,y) is the grayscale value of a pixel block in the neighborhood; I(x′,y′) is the grayscale value of a neighbor pixel block in the neighborhood.

[0215] In some embodiments, the calculation formula of the preset mapping function is:

[0216]

[0217] Wherein, I is the first grayscale value, M is the upper limit of the first grayscale value, K is the mapping slope; and C is the equivalent approximate concentration.

[0218] In some embodiments, the first determining module 102 is further configured to determine an equivalent approximate concentration based on a preset quadratic term fitting substance concentration library.

[0219] Figure 9 Schematic diagram of the structure of an electronic device provided in the embodiment of the present application. Correspondingly, the embodiment of the present application also provides an electronic device, please refer to Figure 9 The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned image processing method are implemented. Since the image processing method has been described in detail above, it will not be repeated here.

[0220] Accordingly, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned image processing method. Since the image processing method has been described in detail above, it will not be repeated here.

[0221] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0222] The above is a detailed introduction to the image processing method, system, device and storage medium provided in the embodiments of the present application, and specific examples are used to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the technical solution and core idea of ​​the present application; ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solution of the embodiments of the present application.

Claims

1. An image processing method, characterized in that: Applied to a passive Fourier infrared telemetry system, the method comprises: Obtaining a detection spectrum and geometric parameters of the passive Fourier infrared telemetry system; When the detection spectrum contains the target substance, determining an equivalent approximate concentration of the target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier transform infrared telemetry system; Determining a first grayscale value of the equivalent approximate concentration according to the equivalent approximate concentration and a preset mapping function; Determine a first grayscale plane layer according to the first grayscale value and the geometric parameter; According to the first grayscale plane layer, a target concentration situation diffusion trend map of the target substance is determined according to a preset interpolation algorithm.

2. The image processing method according to claim 1, wherein: The geometric parameters include: optical path parameters, coordinate parameters and motion state parameters; the detection spectrum includes a plurality of detection points of the target substance; The method for determining the first grayscale plane layer includes: Constructing a grayscale plane of the target substance according to the optical path parameters; The first grayscale plane layer is determined according to the coordinate parameters, the motion state parameters, the first grayscale value of each detection point, and the grayscale plane.

3. The image processing method according to claim 2, wherein: Also includes: Divide the grayscale plane into m*n pixel blocks, where m and n are positive integers; Constructing a rectangular coordinate system with the target vertex angle of the grayscale plane as the origin; Define the coordinates of each detection point in the grayscale plane coordinate system.

4. The image processing method according to claim 2, wherein: The determining the first grayscale plane layer according to the coordinate parameters, the motion state parameters, the first grayscale value of each detection point, and the grayscale plane includes: Determine the target coordinates of each detection point according to the coordinate parameters and the motion state parameters; wherein the target coordinates are used to represent the coordinates of the first grayscale value of each detection point in the grayscale plane; The first grayscale plane layer is constructed according to the target coordinates of all detected points.

5. The image processing method according to claim 1, wherein: The step of determining the target concentration diffusion trend map of the target substance according to the first grayscale plane layer and a preset interpolation algorithm includes: Expanding the first grayscale plane layer to obtain a second grayscale plane layer; Determine a first mapped grayscale value of the second grayscale plane layer according to a preset bilinear interpolation algorithm; determining a first neighborhood grayscale difference according to the first mapped grayscale value and a preset neighborhood grayscale difference model; When the first neighborhood grayscale difference is greater than a preset grayscale difference value, determining a second neighborhood grayscale difference according to a preset neighboring interpolation algorithm and the first mapped grayscale value; When the grayscale difference of the second neighborhood is greater than the preset grayscale difference value, return to repeatedly perform the operation of expanding the first grayscale plane layer, and determine the target concentration situation diffusion trend map when the grayscale difference of the second neighborhood is less than the preset grayscale difference value.

6. The image processing method according to claim 5, characterized in that The method for determining the second neighborhood grayscale difference includes: When the grayscale difference of the first neighborhood is greater than a preset grayscale difference value, determining a second mapped grayscale value of the second grayscale plane layer according to a preset neighboring interpolation algorithm; determining a second grayscale value according to the first mapped grayscale value and the second mapped grayscale value; The second neighborhood grayscale difference is determined according to the second grayscale value and the preset neighborhood grayscale difference model.

7. The image processing method according to claim 5, characterized in that: The determining the target concentration situation diffusion trend map when the grayscale difference of the second neighborhood is less than the preset grayscale difference value includes: The target grayscale value of the second grayscale plane layer is determined until the grayscale difference of the second neighborhood is less than the preset grayscale difference value, and the second grayscale plane layer is reduced to obtain the target concentration situation diffusion trend map.

8. The image processing method according to claim 5, wherein: The calculation formula of the preset neighborhood grayscale difference model is: ΔI i =|I(x,y)-I(x ′ ,y ′ )|; Among them, S(x,y) is the neighborhood grayscale difference; AvgDiff(x,y) is the mean of the neighborhood grayscale difference; I max is the maximum grayscale value; N is the number of pixel blocks in the neighborhood; ΔI i is the grayscale difference of the i-th neighbor; I(x,y) is the grayscale value of a pixel block in the neighborhood; I(x ′ ,y ′ ) is the grayscale value of a neighboring pixel block in the neighborhood.

9. The image processing method according to claim 1, wherein: The calculation formula of the preset mapping function is: Wherein, I is the first grayscale value, M is the upper limit of the first grayscale value, K is the mapping slope; and C is the equivalent approximate concentration.

10. The image processing method according to claim 1, wherein: The method for determining the equivalent approximate concentration comprises: The equivalent approximate concentration is determined by fitting a substance concentration library according to a preset quadratic term.

11. An image processing system, characterized in that: include: An acquisition module is used to obtain the detection spectrum and geometric parameters of the passive Fourier infrared telemetry system; a first determining module, configured to determine an equivalent approximate concentration of the target substance when the detection spectrum contains the target substance; wherein the equivalent approximate concentration is used to characterize the concentration of the target substance in the passive Fourier transform infrared telemetry system; A second determining module, configured to determine a first grayscale value of the equivalent approximate concentration according to the equivalent approximate concentration and a preset mapping function; a third determining module, configured to determine a first grayscale plane layer according to the first grayscale value and the geometric parameter; The fourth determining module is used to determine the target concentration situation diffusion trend map of the target substance according to the first grayscale plane layer and a preset interpolation algorithm.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the image processing method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the image processing method according to any one of claims 1 to 10 is implemented.

Images