A method for generating infrared simulation images under ocean background

Abstract

The present invention relates to a method for generating an infrared simulation image under an ocean background, and belongs to the field of infrared simulation technology. The present invention performs correlation recognition on features inside the image through the generated infrared simulation image, determines the waveform features of the corresponding infrared waves based on the infrared waves associated with different areas in the corresponding simulation image, and then performs correlation confirmation of values ​​based on the specific waveform features, and then performs correlation classification of several infrared waves based on the confirmed specific values, so as to lock the corresponding infrared waves of the same type, and lock the relevant areas with relatively consistent corresponding features based on the determined infrared waves of the same type, and facilitate the subsequent specific optimization of the image based on the determined different areas, so as to solve the problem of not performing correlation division of features for the corresponding detection area, and achieve a better regional optimization effect.

Description

Technical Field

[0001] The invention relates to a method for generating an infrared simulation image under an ocean background, and belongs to the technical field of infrared simulation. Background Art

[0002] The generation of infrared simulation images is usually based on the understanding and modeling of the radiation characteristics of real objects in the infrared band. By analyzing the influence of factors such as the material, temperature, and surface characteristics of the object on infrared radiation, mathematical models and physical laws are used to calculate the infrared radiation intensity and distribution of the object under different conditions.

[0003] The application with publication number CN113470136A discloses a method, device and electronic device for generating infrared simulation images of the sea surface, which relates to the field of computer technology. The specific implementation scheme is: constructing a grid in a projection space coordinate system, and a coordinate conversion strategy from a world space coordinate system to a projection space coordinate system; determining the zero-line-of-sight infrared radiation data of the sea surface, and a first distance from the camera to the sea surface; selecting a target level that matches the first distance from the grids of multiple levels, wherein the number of grid points in grids of different levels is different; generating an infrared simulation image of the sea surface in the projection space coordinate system according to the zero-line-of-sight infrared radiation data, the coordinate conversion strategy and the grid of the target level. As a result, when constructing a large ocean scene, the rapid rendering of the sea surface model can also be achieved, thereby improving the real-time performance when generating infrared simulation images of the sea surface.

[0004] When generating infrared simulation images against an ocean background, there will be a large amount of reflected light on the surface of the ocean environment. Therefore, during the actual simulation processing, the infrared images generated in some areas will be blurred. The reason for the blur is that the corresponding regional features have not been distinguished, resulting in the generated simulation images still having deficiencies in simulation display. Therefore, it is necessary to perform relevant feature division for the corresponding detection area, divide the corresponding detection area into multiple related areas with different reflection features, and then perform relevant optimization for different related areas, so as to ensure the overall optimization display effect of the corresponding simulation image. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for generating infrared simulation images under an ocean background, which solves the problem of not performing relevant division of features for corresponding detection areas.

[0006] The invention discloses a method for generating an infrared simulation image under an ocean background, which is characterized in that it comprises the following steps:

[0007] Step 1: Based on the defined detection area and the associated radio waves generated by the detection area in the current environment, an infrared simulation image belonging to the detection area is directly generated, wherein the detection area is calibrated in advance by the associated personnel;

[0008] Step 2: Based on the generated infrared simulation image, waveform analysis is performed on several groups of infrared waves associated with the infrared simulation image. Based on the waveform characteristics of different infrared waves, similar characteristic waves are determined for related infrared waves with similar characteristics. Based on the determined similar characteristic waves, similar areas are locked and calibrated in the infrared simulation image. The specific sub-steps are as follows:

[0009] S21. Based on several groups of infrared waves associated with the infrared simulation image, the wavelength and peak difference of each group of infrared waves are confirmed, and the peak turning points related to the infrared waves are preferentially determined. The change trends of the adjacent points before and after the peak turning points are opposite. A group of points are randomly selected, and the adjacent points before this point are proposed as the leading points, and the adjacent points after this point are proposed as the trailing points. The waveform trend segment between the leading point and this point is marked as the leading segment, and the waveform trend segment between this point and the trailing point is marked as the trailing segment. The point associated with the leading segment value trend being climbing and the trailing segment value trend being decreasing is marked as the peak turning point, and the lateral horizontal distance L between adjacent peak turning points is confirmed in turn. i , where i represents different adjacent peak turning points, and several groups of horizontal distances L belonging to this infrared wave i Perform mean processing and lock the first eigenvalue T1 k , where k represents different infrared waves;

[0010] S22, determine the valley turning point of the corresponding infrared wave again, the change trends of the adjacent points before and after the valley turning point are opposite, adopt the same processing method as that of determining the front segment and the rear segment in step S21, the numerical trend of the front segment of the valley turning point is downward, and the numerical change trend of the rear segment is upward, based on the peak turning point and the valley turning point marked in the corresponding infrared wave, identify the vertical and horizontal distances between the adjacent peak turning points and the valley turning points, and then average the determined groups of vertical and horizontal distances to lock the second eigenvalue T2 k , where k represents different infrared waves;

[0011] S23, lock the standard feature TZ corresponding to the infrared wave k , its TZ k =T1 k ×C1+T2 k ×C2 locks its TZ k , where C1 and C2 are preset fixed coefficient factors;

[0012] S24, standard features TZ of several groups of infrared waves associated with infrared simulation images k Confirm them one by one, sort them from small to large, determine the standard feature sequence, and select the minimum value Tz from this standard feature sequence K min, will belong to [Tz K min,Tz K min+Y1] are divided into similar features, the corresponding infrared waves are calibrated as similar characteristic waves, the standard features divided into similar features in the standard feature sequence are eliminated, and then the minimum value is re-determined, and then the second group of similar characteristic waves are confirmed, and so on, and the subsequent associated groups of similar characteristic waves are confirmed one by one, where Y1 is the preset value;

[0013] Step 3: Based on the similar areas marked by the infrared simulation image, corresponding Gaussian noise is added to each different similar area in the infrared simulation image, and the infrared simulation image after the Gaussian noise is added is marked as an enhanced image. The specific sub-steps are:

[0014] S31. Identify the average radiation intensity of each different similar area from the infrared simulation image, and calibrate the average radiation intensity of the corresponding similar area as FS o , where o represents different homogeneous regions;

[0015] S32, the FS generated by each similar area o Perform ratio processing to determine several FS o The ratio sequence is then used to extract noise from the set noise interval, and the extracted noise is calibrated as ZS o , and ZS o ∈ noise interval, keep the extracted noise ZS corresponding to the same area o The resulting ratio sequence is similar to FS o The ratio sequence is consistent with that of , and the noise interval is the preset interval;

[0016] S33: Based on the benchmark set in step S32, o When the value of the changed ZS o When the variance between them is at its maximum, the ZS determined at this moment o As an added value, it is added to the corresponding similar area in the infrared simulation image;

[0017] When adding Gaussian noise, it cannot exceed the gray value range of the image;

[0018] Step 4: Based on the calibrated enhanced image, adjust the grayscale value of each different similar area in the enhanced image. Based on the contrast performance of several similar areas after adjustment, select the best grayscale value in the adjustment process, and display the optimized enhanced image in association. The sub-steps are:

[0019] S41, based on the set gray value range, the gray values ​​of different similar areas are adjusted and changed, and based on each adjustment change process, the contrast of several similar areas is identified, and the range value of the gray value range is a preset value, and each ratio within the contrast is extracted and calibrated as DB in turn o , where o represents different similar areas, and o=1, 2, ..., m;

[0020] S42. Locking several ratio DBs o The mean of Lock the corresponding approved value BB o , based on the specific gray value change process, determine the approved value BB corresponding to each change process o ;

[0021] When BB o When it is at the maximum value, the gray value change process is calibrated as the standard process, and the gray value corresponding to the standard process is calibrated as the optimal gray value and executed, and the enhanced image after optimization is displayed in association.

[0022] The present invention provides a method for generating infrared simulation images under an ocean background. Compared with the prior art, it has the following beneficial effects:

[0023] The present invention uses the generated infrared simulation image to perform relevant recognition of the features inside the image, determines the waveform features of the corresponding infrared waves based on the infrared waves associated with different areas in the corresponding simulation image, and then performs relevant confirmation of the values ​​based on the specific waveform features. Subsequently, based on the confirmed specific values, several infrared waves are associated and classified to lock the corresponding infrared waves of the same type. Based on the determined infrared waves of the same type, the related areas with relatively consistent corresponding features are locked. Based on the determined different areas, the specific optimization of the image is facilitated to achieve better regional optimization effects.

[0024] For the specifically divided related areas, Gaussian noise is added first, and then the pixel value of the overall image after the noise is added is adjusted. Based on the specific adjustment process, the contrast change of each area is identified, and based on the specific change state of the contrast value, the corresponding approved value is locked. Then, based on the different approved values ​​corresponding to different change processes, the best grayscale value is selected and executed to achieve the best optimization effect of the image, so that each different related area of ​​this type of infrared simulation image can be fully optimized, so that the displayed infrared simulation image can achieve better display effect in terms of display. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of the present invention;

[0026] Figure 2 This is a specific optimization schematic diagram of the image enhancement of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0028] See also Figure 1 The present application provides a method for generating an infrared simulation image under an ocean background, comprising the following steps:

[0029] Step 1: Based on the defined detection area and the associated radio waves generated by the detection area in the current environment, an infrared simulation image belonging to the detection area is directly generated. The detection area is calibrated in advance by the associated personnel. The generated infrared simulation image is a preliminary simulation image, which is relatively rough and needs to be optimized. In the corresponding physical environment, as long as the object has a temperature higher than absolute zero, it will radiate electromagnetic waves outward, and its electromagnetic waves contain relevant infrared bands. Based on the relevant infrared bands fed back by different points in the detection area, an infrared simulation image of the entire detection area can be generated. The principle of infrared simulation is to simulate the image based on the intensity of the relevant electromagnetic waves emitted by the corresponding object;

[0030] Step 2: Based on the generated infrared simulation image, waveform analysis is performed on several groups of infrared waves associated with the infrared simulation image. Based on the waveform characteristics of different infrared waves, related infrared waves with similar characteristics are determined to be similar characteristic waves. Based on the determined similar characteristic waves, similar areas are locked and calibrated in the infrared simulation image. Specifically, the corresponding simulation image is generated in real time based on the feedback infrared waves. Since the reflectivity and temperature data of each different area are different, the infrared waves generated by each different area are also different. Therefore, based on the corresponding infrared waves of different areas, the related areas with similar infrared characteristics can be calibrated, which is convenient for subsequent image optimization.

[0031] The specific sub-steps of waveform analysis for several groups of infrared waves are as follows:

[0032] S21. Based on several groups of infrared waves associated with the infrared simulation image, the wavelength and peak difference of each group of infrared waves are confirmed, and the peak turning points related to the infrared waves are preferentially determined. The change trends of the adjacent points before and after the peak turning points are opposite. A group of points are randomly selected, and the adjacent points before this point are proposed as the leading points, and the adjacent points after this point are proposed as the trailing points. The waveform trend segment between the leading point and this point is marked as the leading segment, and the waveform trend segment between this point and the trailing point is marked as the trailing segment. The point associated with the leading segment value trend being climbing and the trailing segment value trend being decreasing is marked as the peak turning point, and the horizontal distance L between adjacent peak turning points is confirmed in turn (that is, the vertical horizontal distance. If the two peak turning points are not at the same height, the horizontal distance between the two points is determined based on the vertical line where the corresponding peak turning points are located). i , where i represents different adjacent peak turning points, and several groups of horizontal distances L belonging to this infrared wave i Perform mean processing and lock the first eigenvalue T1 k , where k represents different infrared waves;

[0033] S22, determine the valley turning point of the corresponding infrared wave again, the change trends of the adjacent points before and after the valley turning point are opposite, adopt the same processing method to determine the front segment and the rear segment in step S21, the numerical trend of the front segment of the valley turning point is downward, and the numerical change trend of the rear segment is upward (that is, part of the line segment in front of this point continues to decline, and part of the line segment behind it continues to rise, which is opposite to the correlation method of the determined peak turning point), based on the peak turning point and valley turning point calibrated in the corresponding infrared wave, identify the vertical and horizontal distances between the adjacent peak turning points and valley turning points (in the corresponding infrared wave, the relevant peak points and valley points are locked, and the relevant values ​​between the corresponding points can reflect the relevant waveform characteristics of the corresponding infrared wave, and the vertical and horizontal distance is the vertical distance between the horizontal base surfaces where the two adjacent points are located), and then average the determined groups of vertical and horizontal distances to lock the second eigenvalue T2 k , where k represents different infrared waves;

[0034] S23, lock the standard feature TZ corresponding to the infrared wave k , its TZ k =T1 k ×C1+T2 k ×C2 locks its TZ k , where C1 and C2 are preset fixed coefficient factors, and their specific values ​​are determined by the operator based on experience;

[0035] S24, standard features TZ of several groups of infrared waves associated with infrared simulation images k Confirm them one by one, sort them from small to large, determine the standard feature sequence, and select the minimum value Tz from this standard feature sequence K min, will belong to [Tz K min,Tz K min+Y1] are divided into similar features, the corresponding infrared waves are calibrated as similar characteristic waves, the standard features divided into similar features in the standard feature sequence are eliminated, and then the minimum value is re-determined, and then the second group of similar characteristic waves are confirmed, and so on, and several groups of similar characteristic waves associated subsequently are confirmed one by one, where Y1 is a preset value, and its specific value is determined by the operator based on experience;

[0036] For example: the proposed standard feature sequence is {22, 24, 27, 28, 29, 29.5, 31, 34}, and the proposed Y1 value is 4. Based on the corresponding relevant standards, the corresponding division interval is determined, and the standard features of 22 and 24 are divided into the same type of features. Starting from 27, the standard features of 27, 28, 29, 29.5, and 31 are divided into the same type of features. And so on, the same type of feature waves are confirmed one by one;

[0037] Step 3: Based on the similar areas marked by the infrared simulation image, corresponding Gaussian noise is added to each different similar area in the infrared simulation image, and the infrared simulation image after the Gaussian noise is added is marked as an enhanced image, wherein the specific sub-steps of adding Gaussian noise are as follows:

[0038] S31. Identify the average radiation intensity of each different similar area from the infrared simulation image, and calibrate the average radiation intensity of the corresponding similar area as FS o , where o represents different homogeneous regions;

[0039] S32, the FS generated by each similar area o Perform ratio processing to determine several FS o The ratio sequence is then used to extract noise from the set noise interval, and the extracted noise is calibrated as ZS o , and ZS o ∈ noise interval, keep the extracted noise ZS corresponding to the same area o The resulting ratio sequence is similar to FS o The ratio sequence is consistent with that of the noise interval, and the noise interval is a preset interval, which is prepared in advance by relevant operators;

[0040] S33: Based on the benchmark set in step S32, o When the value of the changed ZS o When the variance between them is at its maximum, the ZS determined at this moment o As an added value, it is added to the corresponding similar area in the infrared simulation image. Specifically, its variance value is: Prioritize to determine several ZS o The mean value between them is marked as Zj, and o=1, 2, ..., n is proposed. Determine the corresponding variance value Fc;

[0041] Specifically, the generated noise array is added pixel by pixel to the corresponding similar area. It is ensured that in the process of adding noise, the grayscale value range of the image will not be exceeded or data overflow will be caused. The pixel value after adding noise can be appropriately restricted or adjusted to ensure the validity of the image. Therefore, it is necessary to restrict the added corresponding noise to belong to the corresponding noise interval, and add the corresponding Gaussian noise to each different similar area of ​​the infrared simulation image, so as to more realistically simulate the noise situation in the actual environment and provide more challenging and realistic test data for subsequent analysis and processing.

[0042] Step 4: Based on the calibrated enhanced image, adjust the grayscale value of each different similar area in the enhanced image. Based on the contrast performance of several similar areas after adjustment, select the best grayscale value in the adjustment process, and display the optimized enhanced image in association. Specifically, this part is to optimize the enhanced image again. In order to make each different area more obvious in contrast, adjust the grayscale value inside it. During the adjustment, remap the grayscale value of the original area to a new grayscale range to achieve different visual effects or meet specific display requirements.

[0043] Among them, combined Figure 2 , the sub-steps for optimizing the enhanced image are:

[0044] S41. Based on the set grayscale value range, the grayscale values ​​of different similar areas are adjusted and changed, and based on each adjustment and change process, the contrast of several similar areas is identified (the contrast can be regarded as a ratio sequence), and the range value of the grayscale value range is a preset value, and the specific value is formulated by the relevant operator based on experience. Each ratio in the contrast is extracted and calibrated as DB in turn. o , where o represents different similar areas, and o=1, 2, ..., m;

[0045] S42. Locking several ratio DBs o The mean of Lock the corresponding approved value BB o , based on the specific gray value change process, determine the approved value BB corresponding to each change process o , when BB o When it is at the maximum value, the gray value change process is calibrated as the standard process, and the gray value corresponding to the standard process is calibrated as the optimal gray value and executed, and the enhanced image after optimization is displayed in association.

[0046] Specifically, during adjustment, the grayscale values ​​within each different similar area are adjusted synchronously. In each adjustment process, the corresponding pixel values ​​within the same area are adjusted, and the resulting contrast will change synchronously. When the contrast of each area reaches the maximum state, that is, the state with the most obvious contrast after adjustment, then each different associated area of ​​this type of infrared simulation image can be fully optimized, so that the displayed infrared simulation image can achieve better display effect in terms of display.

[0047] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0048] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for generating an infrared simulation image under an ocean background, characterized in that: The following steps are involved: Step 1: Based on the defined detection area and the associated radio waves generated by the detection area in the current environment, an infrared simulation image belonging to the detection area is directly generated, wherein the detection area is calibrated in advance by the associated personnel; Step 2: Based on the generated infrared simulation image, waveform analysis is performed on several groups of infrared waves associated with the infrared simulation image, and based on the waveform characteristics of different infrared waves, similar characteristic waves are determined for related infrared waves with similar characteristics, and then similar areas are locked and calibrated in the infrared simulation image based on the determined similar characteristic waves; Step 3: based on the similar regions marked by the infrared simulation image, corresponding Gaussian noise is added to each different similar region in the infrared simulation image, and the infrared simulation image after the Gaussian noise is added is marked as an enhanced image; Step 4: Based on the calibrated enhanced image, adjust the grayscale value of each different similar area in the enhanced image, select the best grayscale value in the adjustment process based on the contrast performance of several similar areas after adjustment, and display the optimized enhanced image in association; In step 2, the specific sub-steps of performing waveform analysis on a plurality of groups of infrared waves are: S21. Based on several groups of infrared waves associated with the infrared simulation image, the wavelength and peak difference of each group of infrared waves are confirmed, and the peak turning points related to the infrared waves are preferentially determined. The change trends of the adjacent points before and after the peak turning points are opposite. A group of points are randomly selected, and the adjacent points before this point are proposed as the leading points, and the adjacent points after this point are proposed as the trailing points. The waveform trend segment between the leading point and this point is marked as the leading segment, and the waveform trend segment between this point and the trailing point is marked as the trailing segment. The point associated with the leading segment value trend being climbing and the trailing segment value trend being decreasing is marked as the peak turning point, and the lateral horizontal distance L between adjacent peak turning points is confirmed in turn. i , where i represents different adjacent peak turning points, and several groups of horizontal distances L belonging to this infrared wave i Perform mean processing and lock the first eigenvalue T1 k , where k represents different infrared waves; S22, determine the valley turning point of the corresponding infrared wave again, the change trends of the adjacent points before and after the valley turning point are opposite, adopt the same processing method as that of determining the front segment and the rear segment in step S21, the numerical trend of the front segment of the valley turning point is downward, and the numerical change trend of the rear segment is upward, based on the peak turning point and the valley turning point marked in the corresponding infrared wave, identify the vertical and horizontal distances between the adjacent peak turning points and the valley turning points, and then average the determined groups of vertical and horizontal distances to lock the second eigenvalue T2 k , where k represents different infrared waves; S23, lock the standard feature TZ corresponding to the infrared wave k , its TZ k =T1 k ×C1+T2 k ×C2 locks its TZ k , where C1 and C2 are preset fixed coefficient factors; S24, standard features TZ of several groups of infrared waves associated with infrared simulation images k Confirm them one by one, sort them from small to large, determine the standard feature sequence, and select the minimum value Tz from this standard feature sequence K min, will belong to [Tz K min,Tz K min+Y1] are divided into similar features, the corresponding infrared waves are calibrated as similar feature waves, the standard features divided into similar features in the standard feature sequence are eliminated, and then the minimum value is re-determined, and then the second group of similar feature waves are confirmed, and so on, and several subsequent associated groups of similar feature waves are confirmed one by one, where Y1 is the preset value.

2. The method for generating an infrared simulation image under an ocean background according to claim 1, characterized in that: In step 3, the specific sub-steps of adding Gaussian noise are: S31. Identify the average radiation intensity of each different similar area from the infrared simulation image, and calibrate the average radiation intensity of the corresponding similar area as FS o , where o represents different homogeneous regions; S32, the FS generated by each similar area o Perform ratio processing to determine several FS o The ratio sequence is then used to extract noise from the set noise interval, and the extracted noise is calibrated as ZS o , and ZS o ∈ noise interval, keep the extracted noise ZS corresponding to the same area o The resulting ratio sequence is similar to FS o The ratio sequence is consistent with that of , and the noise interval is the preset interval; S33: Based on the benchmark set in step S32, o When the value of the changed ZS o When the variance between them is at its maximum, the ZS determined at this moment o As an added value, it is added to the corresponding similar area in the infrared simulation image.

3. The method for generating infrared simulation images under ocean background according to claim 2, characterized in that: In the step S33, when the Gaussian noise is added, it cannot exceed the gray value range of the image.

4. The method for generating infrared simulation images under ocean background according to claim 1, characterized in that: In step 4, the sub-steps for optimizing the enhanced image are: S41, based on the set gray value range, the gray values ​​of different similar areas are adjusted and changed, and based on each adjustment change process, the contrast of several similar areas is identified, and the range value of the gray value range is a preset value, and each ratio within the contrast is extracted and calibrated as DB in turn o , where o represents different similar areas, and o = 1, 2, ..., m; S42. Locking several ratio DBs o The mean of Lock the corresponding approved value BB o , based on the specific gray value change process, determine the approved value BB corresponding to each change process o .

5. The method for generating infrared simulation images under ocean background according to claim 4, characterized in that: In step S42, when BB o When it is at the maximum value, the gray value change process is calibrated as the standard process, and the gray value corresponding to the standard process is calibrated as the optimal gray value and executed, and the enhanced image after optimization is displayed in association.

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