Infrared simulation image generation method under ocean background

Abstract

The invention relates to an infrared simulation image generation method under an ocean background, and belongs to the technical field of infrared simulation. The method comprises the following steps: performing correlation identification on internal characteristics of an image through a generated infrared simulation image, determining waveform characteristics of corresponding infrared waves based on the infrared waves associated with different areas in the corresponding simulation image, performing correlation confirmation on numerical values based on the specific waveform characteristics, and subsequently performing correlation identification on the numerical values based on the confirmed specific numerical values. According to the method, a plurality of infrared waves are associated and classified, so that corresponding infrared waves of the same kind are locked, related areas with relatively consistent corresponding features are locked based on the determined infrared waves of the same kind, and specific optimization of images is facilitated subsequently based on the determined different areas. The problem that related division of the features is not carried out for the corresponding detection area is solved, and a good area optimization effect is achieved.

Description

Technical Field

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

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

[0003] Application Publication No. CN113470136A discloses a method, device, and electronic device for generating infrared simulated images of the sea surface, relating to the field of computer technology. The specific implementation scheme is as follows: constructing a grid in a projected space coordinate system and a coordinate conversion strategy from a world space coordinate system to a projected space coordinate system; determining 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 multiple levels of the grid, wherein the number of grid points in different levels of grids is different; and generating an infrared simulated image of the sea surface in the projected space coordinate system based on the zero-line-of-sight infrared radiation data, the coordinate conversion strategy, and the grid of the target level. Thus, when constructing large ocean scenes, rapid rendering of the sea surface model can also be achieved, thereby improving the real-time performance of generating infrared simulated images of the sea surface.

[0004] When generating infrared simulation images against the background of the ocean, 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 blurry. The reason for the blur is that the corresponding regional features are not distinguished, resulting in the generated simulation images still having deficiencies in simulation display. Therefore, it is necessary to perform feature division for the corresponding detection area, divide the corresponding detection area into multiple related areas with different reflection features, and then perform related optimization for different related areas to ensure the overall optimization display effect of the corresponding simulation image. Summary of the Invention

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

[0006] The invention provides a method for generating an infrared simulation image under an ocean background, which is special in that it comprises the following steps: Step 1: Based on the defined detection area and the associated radio waves generated by the detection area in the current environment, directly generate an infrared simulation image belonging to the detection area, wherein the detection area is calibrated in advance by the relevant 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. Based on the waveform characteristics of different infrared waves, related infrared waves with similar characteristics are identified as similar characteristic waves. Based on the identified similar characteristic waves, similar areas are locked and calibrated within the infrared simulation image. The specific sub-steps are as follows: 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 point are opposite. A group of points are randomly selected, and the group of adjacent points before this point is proposed as the leading point, and the group of adjacent points after this point is proposed as the trailing point. 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 climbing and the trailing segment value trend decreasing is marked as the peak turning point, and the horizontal distance L between adjacent peak turning points is confirmed in sequence. 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, then determine the valley turning point of the corresponding infrared wave. The change trends of the adjacent points before and after the valley turning point are opposite. The same processing method is used 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 trend of the rear segment is upward. Based on the peak turning point and valley turning point marked in the corresponding infrared wave, the vertical and horizontal distances between the adjacent peak turning points and valley turning points are identified. Then, the determined groups of vertical and horizontal distances are averaged to lock the second eigenvalue T2 k , where k represents different infrared waves; S23, lock the standard characteristic 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 kConfirm them one by one, sort them in ascending order, 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, and the corresponding infrared waves are calibrated as similar characteristic waves. The standard features that are divided into similar features in the standard feature sequence are eliminated, and then the minimum value is re-determined. Then, the second group of similar characteristic waves is confirmed, and so on. Several groups of subsequent associated similar characteristic waves are confirmed one by one, where Y1 is the preset value; 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 addition is marked as an enhanced image. The specific sub-steps 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 areas; 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 based on the ratio sequence, 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 ratio sequence generated is consistent with FS o The ratio sequence of is consistent, and the noise interval is the preset interval; S33, based on the benchmark set in step S32, o When the value changes, the number of ZS o When the variance between the two is at its maximum, the ZS determined at this moment is o As an added value, it is added to the corresponding similar area in the infrared simulation image; When adding Gaussian noise, it must not exceed the grayscale value range of the image; 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 associate the optimized enhanced image for display. The sub-steps are: S41, based on the set gray value range, adjust the gray values ​​of different similar areas, and based on each adjustment change process, identify the contrast of several similar areas, the range value of their gray value range is the preset value, extract each ratio within the contrast and mark it 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 is marked as JJ, using 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 grayscale value change process is calibrated as the standard process, and the grayscale value corresponding to the standard process is calibrated as the optimal grayscale value and executed, and the enhanced image after optimization is displayed in association.

[0007] The present invention provides a method for generating infrared simulation images under an ocean background. Compared with the existing technology, it has the following advantages: The present invention uses the generated infrared simulation image to perform correlation identification on the features within 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 correlation confirmation of values ​​based on the specific waveform features. Subsequently, based on the confirmed specific values, correlation classification of several infrared waves is performed to lock in the corresponding infrared waves of the same type. Based on the determined infrared waves of the same type, the relevant areas with relatively consistent corresponding features are locked in. Based on the determined different areas, the subsequent specific optimization of the image is facilitated to achieve better regional optimization effects. For the specific divided related areas, Gaussian noise is added first, and then the pixel value of the overall image after noise addition 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

[0008] Figure 1 Schematic diagram of the process of the present invention; Figure 2 This is a specific optimization schematic diagram of image enhancement according to the present invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention. Example

[0010] See also Figure 1 , the present application provides a method for generating an infrared simulation image under an ocean background, comprising the following steps: Step 1: Based on the defined detection area and the associated radio waves generated by this detection area in the current environment, an infrared simulation image belonging to this detection area is directly generated. The detection area is calibrated in advance by the relevant 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. Its electromagnetic waves contain relevant infrared bands. Based on the relevant infrared bands fed back from different points in this detection area, an infrared simulation image of the entire detection area can be generated. The principle of infrared simulation is to perform image simulation based on the intensity of the relevant electromagnetic waves emitted by the corresponding object; 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, related areas with similar infrared characteristics can be calibrated, which is convenient for subsequent image optimization. The specific sub-steps of performing waveform analysis on several groups of infrared waves are as follows: 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 point are opposite. A group of points are randomly selected, and the group of adjacent points before this point is proposed as the leading point, and the group of adjacent points after this point is proposed as the trailing point. 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 an increasing numerical trend of the leading segment and a decreasing numerical trend of the trailing segment is marked as a peak turning point. The horizontal distance L between adjacent peak turning points is confirmed in sequence (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; S22, then determine the valley turning point of the corresponding infrared wave, the change trends of the adjacent points before and after the valley turning point are opposite, and the same processing method for determining the front segment and the rear segment in step S21 is adopted, 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, the part of the line segment in front of this point continues to decline, and the 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; S23, lock the standard characteristic 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; S24. Standard features TZ of several groups of infrared waves associated with infrared simulation images k Confirm them one by one, sort them in ascending order, determine the standard feature sequence, and select the minimum value Tz from this standard feature sequenceK min, will belong to [Tz K min, Tz K min+Y1] are divided into similar features, and 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. Then, the second group of similar characteristic waves is confirmed, and so on. Several groups of subsequent associated similar characteristic waves are confirmed one by one, where Y1 is a preset value, and its specific value is determined by the operator based on experience; 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 similar features. Then, starting from 27, the standard features of 27, 28, 29, 29.5, and 31 are divided into similar features. And so on, the similar feature waves are confirmed one by one. 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 addition is marked as an enhanced image. The specific sub-steps of adding Gaussian noise are as follows: 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 areas; 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 based on the ratio sequence, 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 ratio sequence generated is consistent with FS o The ratio sequence is consistent with that of the noise interval, which is a preset interval and is prepared in advance by relevant operators; S33, based on the benchmark set in step S32, o When the value changes, the number of ZS o When the variance between the two is at its maximum, the ZS determined at this moment is 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; Specifically, the generated noise array is added pixel by pixel to the corresponding similar regions. This ensures that the noise addition process does not exceed the image's grayscale value range or cause data overflow. Appropriate limits or adjustments can be applied to the pixel values ​​after noise addition to ensure image validity. Therefore, the added noise must be constrained to fall within the corresponding noise interval. By adding corresponding Gaussian noise to each different similar region of the infrared simulated image, the resulting simulation more realistically simulates the noise conditions in a real environment, providing more challenging and realistic test data for subsequent analysis and processing.

[0011] 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 a correlated manner. Specifically, this part is to optimize the enhanced image again. In order to make the contrast of each different area more obvious, adjust the grayscale value within it. During the adjustment, the grayscale value of the original area is remapped to a new grayscale range to achieve different visual effects or meet specific display requirements. Among them, combined Figure 2 , the specific sub-steps for optimizing the enhanced image are: S41. Based on the set grayscale value range, adjust the grayscale values ​​of different similar areas, and based on each adjustment change process, identify the contrast of several similar areas (the contrast can be regarded as a ratio sequence). The range value of the grayscale value range is a preset value, and the specific value is determined by the relevant operator based on experience. 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 is marked as JJ, using 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 grayscale value change process is calibrated as the standard process, and the grayscale value corresponding to the standard process is calibrated as the optimal grayscale value and executed, and the enhanced image after optimization is displayed in association.

[0012] Specifically, during adjustment, the grayscale values ​​within each different similar area are adjusted synchronously. In each adjustment process, the corresponding pixel values ​​within the similar 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.

[0013] 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.

[0014] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. 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. 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 a correlated manner.

2. The method for generating an infrared simulation image under an ocean background according to claim 1, characterized in that: 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.

3. The method for generating infrared simulation images under 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.

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

5. 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 .

6. The method for generating infrared simulation images under ocean background according to claim 5, 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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