An infrared turbulence image compensation method based on separation of local drift field and radiation scintillation field

Abstract

This invention relates to the fields of infrared image processing, infrared intelligent sensing, infrared image restoration, and long-range imaging compensation, specifically to an infrared turbulence image compensation method based on the separation of local drift field and radiative scintillation field. This invention separates the local drift field and radiative scintillation field to describe the spatial deformation disturbance and thermal response fluctuation disturbance in infrared turbulence degradation, avoiding the simplistic equation of turbulence degradation with ordinary blurring or overall jitter. By constructing turbulence state units, this invention can describe the turbulence influence state in different spatial regions and at different scales, enabling the image compensation process to adaptively process according to the local turbulence intensity. Furthermore, by extracting stable structural anchors, this invention provides a relatively reliable structural reference for multi-frame compensation, reducing structural misalignment, edge ghosting, and target shape distortion caused by unstable reference frame quality.

Description

Technical Field

[0001] This invention relates to the fields of infrared image processing, infrared intelligent sensing, infrared image restoration, and long-distance imaging compensation technology, specifically to an infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field. Background Technology

[0002] Infrared imaging can acquire image information by utilizing the difference in thermal radiation between the target and the background. It has good perception capabilities under conditions such as nighttime, low light, fog, haze, obscured backgrounds, and long-distance observation. Therefore, it is widely used in scenarios such as border monitoring, port surveillance, forest patrol, sea surface observation, low-altitude sensing, and long-range early warning. Compared with visible light imaging, infrared imaging is less dependent on external lighting conditions and can continuously obtain the thermal radiation response of targets and scenes in complex environments.

[0003] In long-range infrared imaging, the target's thermal radiation signal must travel a long atmospheric propagation path before reaching the imaging device. Factors such as uneven atmospheric temperature distribution, changes in air refractive index, thermal flow disturbances, path jitter, and local turbulence can cause degradation phenomena in infrared images, including local distortion, edge drift, image blurring, grayscale flicker, and structural instability. This type of degradation is often referred to as infrared turbulence degradation, and its manifestations differ from ordinary imaging noise or simple motion blur; it simultaneously includes spatial positional disturbances and fluctuations in thermal radiation response.

[0004] In image sequences affected by infrared turbulence, the position of the same target or background structure may drift irregularly across consecutive frames, and the target contour and edge regions may exhibit jitter, bending, or local deformation. Simultaneously, the grayscale response in infrared images is related to the thermal radiation characteristics of the target or background, and turbulence propagation can also cause fluctuations in local thermal response, manifesting as radiation scintillation, grayscale fluctuations, and thermal structural instability. Treating this type of degradation merely as ordinary image blur makes it difficult to simultaneously correct for both spatial drift and radiation scintillation.

[0005] Existing methods for deturbulence removal or image restoration in infrared images typically employ image registration, multi-frame fusion, deblurring, optical flow compensation, image enhancement, or deep learning-based restoration. While these methods can improve image sharpness under certain conditions, they usually compensate for turbulence degradation as a whole, lacking separate characterization of local drift and radiative scintillation. Within the same infrared image, different regions are often affected by turbulence to varying degrees. Target edges, distant building outlines, horizon areas, and background textures may exhibit different drift amplitudes and scintillation intensities. Uniform compensation can easily lead to local overcompensation, undercompensation, or structural misalignment.

[0006] Multi-frame fusion methods typically require selecting a reference frame or constructing an average image. However, in infrared turbulence scenarios, single-frame images may exhibit local distortions, edge ghosting, or thermal response flicker. Directly using low-quality reference frames for registration and fusion can easily lead to structural ghosting, edge misalignment, and distortion of the target's thermal structure in the compensated image. For infrared monitoring systems that require subsequent target detection, recognition, or state assessment, the restored result not only needs to be visually clearer but also needs to maintain the relative thermal radiation relationship between the target and the background.

[0007] Therefore, there is a need for an infrared turbulence image compensation method that can separate and characterize local spatial drift and radiative scintillation in infrared turbulence degradation, and combine stable structural reference, path consistency constraint and thermal radiation consistency maintenance for hierarchical compensation, so as to improve the structural stability, thermal radiation reliability and subsequent sensing availability of infrared images. Summary of the Invention

[0008] The technical solution of the present invention is as follows: An infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field includes the following steps: Step 1, Acquisition of Temporal Infrared Turbulence Images. A sequence of temporal infrared images affected by atmospheric turbulence is acquired, denoted as: , in, Indicates the first Infrared images of each phase, Indicates the number of phases.

[0009] The time-series infrared image sequence is subjected to time synchronization, spatial coarse registration, and radiometric normalization to reduce the impact of imaging device jitter, overall field of view changes, and global grayscale fluctuations on subsequent turbulence analysis, thereby obtaining the infrared image sequence to be compensated.

[0010] Step 2: Separate the local drift field from the radiation scintillation field.

[0011] Based on the local displacement changes, edge position changes, and local grayscale fluctuations between adjacent time phases in the infrared image sequence to be compensated, spatial disturbances and radiation disturbances in infrared turbulence degradation are separated and characterized. Specifically, spatial position changes are used to construct the local drift field, and thermal radiation response changes are used to construct the radiation scintillation field.

[0012] Step 2.1, Local Drift Field Construction. Based on the local positional changes, edge positional changes, and structural morphological changes between adjacent temporal infrared images, a local drift field is constructed to describe the spatial displacement direction, displacement amplitude, and local deformation degree of the same local structure in consecutive temporal phases. The local drift field is represented as: , in, Indicates the first Spatial position of each phase Local drift field response at the location, This represents the local drift estimation function. This step yields the local drift field. .

[0013] Step 2.2, Construction of the Radiative Scintillation Field. Based on the local drift field obtained in Step 2.1, after position compensation of adjacent temporal images, the thermal radiation response changes of the same spatial region in continuous temporal phases are analyzed to construct a radiative scintillation field, which is used to describe the grayscale scintillation, thermal response fluctuations, and radiation instability in the local region. The radiative scintillation field is represented as: , in, Indicates the first Spatial position in each phase The radiative scintillation field response at that location, This represents the radiation scintillation estimation function. This step yields the radiation scintillation field. .

[0014] Step 3: Construct turbulent state units.

[0015] To describe the degree of infrared turbulence influence in different spatial regions and at different scales, turbulent state units are constructed based on the local drift field and radiation scintillation field obtained in step 2, combined with edge stability, local clarity and temporal consistency information.

[0016] Step 3.1, turbulent state unit construction. For the first... The spatial region in the first The turbulent state unit at each scale is represented as: , in, Indicates drift intensity. Indicates the flicker intensity. Indicates edge stability. Indicates local sharpness. This indicates temporal consistency. This step yields the turbulent state unit. .

[0017] Step 3.2, Turbulence State Score Calculation. Based on the turbulence state unit, the degree of influence of infrared turbulence on different regions is quantitatively evaluated, and the turbulence state score is calculated: , in, Indicates the first The spatial region in the first Turbulent state rating at various scales This represents the weighting coefficient. A higher turbulence state score indicates that the region is more significantly affected by turbulence, and therefore has a higher compensation priority in subsequent compensation processes.

[0018] Step 4, extract the stable structure anchor.

[0019] In order to obtain reference information that can stably reflect changes in scene structure, stable structural anchors with relatively stable spatial position, relatively consistent structural morphology, and controllable thermal response changes are extracted from the infrared image sequence to be compensated.

[0020] Step 4.1, Extraction of stable structural anchors. The set of stable structural anchors is represented as follows: , in, Indicates the first A stable structural anchor, This indicates the number of stable structural anchors. This step yields the set of stable structural anchors. .

[0021] Step 4.2, Stability Score Calculation. The reliability of each stable structural anchor is evaluated, and the stability score is calculated: , in, Indicates the first Stability score of a stable structural anchor. Indicates timing consistency. Indicates edge stability. Indicates local sharpness. Indicates drift intensity. Indicates the flicker intensity. This represents the weighting coefficients. This step yields the stability score of the anchor in the stable structure. .

[0022] Step 5: Establish path consistency constraints.

[0023] Since the drift and radiation changes caused by infrared turbulence usually have a certain temporal continuity, a path consistency constraint is established based on the stable structural anchor obtained in step 4 to maintain the continuity of the changes of the same structure in continuous time phases.

[0024] Step 5.1, Path State Construction. For the first... The stable structural anchor is at the first The path state in each time phase is represented as: , in, Indicates the path status. This represents the path state construction function. This step yields the path state. .

[0025] Step 5.2, Establish path consistency constraints. Based on the previously obtained path state, establish path consistency constraints: , in, This represents the path consistency constraint. This represents the path state prediction function.

[0026] The path consistency constraint ensures that the spatial and radiative variations of the same structure remain continuous and smooth across consecutive time phases. This step yields the path consistency constraint term. .

[0027] Step 6: Layered compensation of infrared turbulence images.

[0028] Based on the local drift field, radiation scintillation field, turbulent state unit, stable structural anchor, and path consistency constraint, the infrared image to be compensated is subjected to layered compensation.

[0029] Step 6.1, Layered Compensation. Based on the turbulence state of different regions, compensation is performed on local drift, radiation scintillation, and detail degradation, resulting in a compensated infrared image: , in, Indicates the first Infrared images after phase compensation This represents the infrared turbulence image compensation function, from which the compensated infrared image is obtained. .

[0030] Step 6.2, Maintaining Thermal Radiation Consistency. To maintain the relative thermal radiation between the target area and the background area, thermal radiation consistency constraints are established based on the compensated infrared image obtained in Step 6.1: , in, This represents the thermal radiation consistency constraint term. Indicates the area to be compensated. This represents the thermal radiation structure characterization function. This step yields the thermal radiation uniformity constraint term. .

[0031] Step 7: Output the compensation result together with the turbulence state.

[0032] Output the compensated infrared image and turbulence state information.

[0033] Step 7.1, Turbulence State Output. Based on the compensation results, generate state information such as turbulence intensity level, structural stability, thermal radiation retention, and residual disturbance degree.

[0034] Step 7.2, Compensation Confidence Calculation. Calculate the compensation confidence based on the state information obtained in the previous steps: , in, Indicates the first The reliability of the compensation results for each phase. Indicates the reverse reliability corresponding to the turbulence intensity level. Indicates structural stability. Indicates the degree of heat radiation retention. Indicates the degree of residual disturbance. This represents the weighting coefficient. This step yields the compensation confidence level. It is then output together with the compensated infrared image.

[0035] The beneficial effects of this invention are: First, by separating the local drift field and the radiation scintillation field, the present invention describes the spatial deformation disturbance and thermal response fluctuation disturbance in infrared turbulence degradation separately, thus avoiding the simplistic equation of turbulence degradation with ordinary fuzziness or overall jitter.

[0036] Second, by constructing turbulent state units, this invention can describe the turbulent influence state in different spatial regions and at different scales, enabling the image compensation process to adaptively process according to the local turbulence intensity.

[0037] Third, by extracting stable structural anchors, this invention provides a relatively reliable structural reference for multi-frame compensation, reducing structural misalignment, edge ghosting, and target shape distortion caused by unstable reference frame quality.

[0038] Fourth, the present invention, through path consistency constraints, ensures that the drift path and radiation change path of the same local structure remain continuous in consecutive time phases, thereby reducing jumps, misalignments and instability enhancements during the compensation process.

[0039] Fifth, by maintaining thermal radiation consistency, the present invention enables the compensated infrared image to maintain the relative thermal radiation relationship between the target area and the background area while improving structural stability and clarity, thereby enhancing the applicability of the compensation results to subsequent target detection, recognition, and status judgment.

[0040] Sixth, this invention not only outputs compensated infrared images, but also outputs turbulence intensity level, structural stability, thermal radiation retention, and compensation reliability, providing a joint reference for image results and turbulence status for long-distance infrared monitoring systems. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall process of an infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field in an embodiment of the present invention; Figure 2 This is a schematic diagram of the time-series infrared turbulence image acquisition and preprocessing process in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the separation process of the local drift field and the radiation scintillation field in an embodiment of the present invention; Figure 4 This is a schematic diagram of the turbulent state unit construction process in an embodiment of the present invention; Figure 5 This is a schematic diagram of the stable structure anchor extraction process in an embodiment of the present invention; Figure 6 This is a schematic diagram of the path consistency constraint establishment process in an embodiment of the present invention; Figure 7 This is a schematic diagram of the infrared turbulence image layering compensation process in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the joint output of compensation results and turbulence state in an embodiment of the present invention; Figure 9 These are the turbulence state scoring heatmap, turbulence level region division map, and original infrared reference frame in the embodiments of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention are further described below with reference to the accompanying drawings and technical solutions. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Without departing from the concept of the present invention, those skilled in the art can make substitutions or modifications to the drift estimation method, scintillation estimation method, structural anchor extraction method, compensation method, and parameter settings in each step.

[0043] Example 1: An infrared turbulence image compensation method based on the separation of local drift field and radiative scintillation field, such as... Figure 1 As shown, the process includes acquiring time-series infrared turbulence images, separating local drift fields from radiation scintillation fields, constructing turbulence state units, extracting stable structure anchors, establishing path consistency constraints, performing layered compensation of infrared turbulence images, and jointly outputting the compensation results and turbulence states.

[0044] Step 1: Acquisition of time-series infrared turbulence images.

[0045] like Figure 2As shown, an infrared imaging device is used to continuously observe the area under observation, obtaining a time-series infrared image sequence affected by atmospheric turbulence. The infrared imaging device can be a short-wave infrared imaging device, a mid-wave infrared imaging device, a long-wave infrared imaging device, or a multi-band infrared imaging device. The area under observation can be a border, port, forest, sea surface, urban edge, or low-altitude observation area.

[0046] The acquired time-series infrared image sequence is denoted as: , in, Indicates the first Infrared images of each phase, The number of time phases is indicated. The time-series infrared image sequence is time-synchronized to ensure that each frame has a uniform time order; spatial coarse registration is performed on the image sequence to reduce global offset caused by overall jitter of the imaging platform or changes in the field of view; radiometric normalization is performed on the image sequence to reduce the impact of overall grayscale shift between different frames on subsequent flicker estimation.

[0047] Step 2: Separate the local drift field from the radiation scintillation field.

[0048] like Figure 3 As shown, after obtaining the infrared image sequence to be compensated, the degradation changes between adjacent temporal images are separated. In this embodiment, the spatial position disturbance in infrared turbulence degradation is described as a local drift field, and the thermal response fluctuation is described as a radiation scintillation field.

[0049] For the Infrared images at different times, in spatial location Construct a local drift field: , in, It can be a region matching function, an edge displacement estimation function, a local optical flow estimation function, or a displacement estimation function implemented by a neural network. The local drift field is used to describe the spatial displacement, deformation direction, and deformation amplitude of the same local structure between adjacent time phases.

[0050] The local drift intensity can be expressed as: , in, Indicates spatial location The local displacement vector between adjacent time phases. If a region is strongly affected by turbulence, and its edge position and local structure change significantly between consecutive time phases, then the local drift intensity of that region is high.

[0051] For the same spatial location, construct a radiative scintillation field: , in, It can be a function for calculating local grayscale differences, a function for calculating local variance, a function for calculating the rate of change of thermal response, or a radiation fluctuation estimation function implemented by a neural network. The radiation scintillation field is used to describe the thermal response fluctuations, grayscale scintillation, and radiation instability of the same local region over consecutive time phases.

[0052] The intensity of radiative scintillation can be expressed as: , in, This represents the position compensation amount determined by the local drift field. By compensating for the local drift before calculating the grayscale difference, the interference of spatial misalignment on the estimation of radiative scintillation can be reduced, making the radiative scintillation field more accurately reflect changes in thermal response.

[0053] Step 3: Construct turbulent state units.

[0054] like Figure 4 As shown, in order to describe the turbulence effects in different regions and at different scales, this embodiment constructs turbulence state units based on local drift field, radiation scintillation field, edge stability, local clarity and temporal consistency.

[0055] For the The spatial region in the first The turbulent state unit at each scale is represented as: , in, Indicates drift intensity. Indicates the flicker intensity. Indicates edge stability. Indicates local sharpness. To indicate temporal consistency, the comprehensive state score of the turbulent state element can be expressed as: , in, Indicates the first The spatial region in the first Turbulent state rating at various scales This represents the weighting coefficient. A higher turbulence state score indicates that the region is more significantly affected by turbulence, and should be given a higher compensation priority in subsequent compensation processes.

[0056] Step 4, extract the stable structure anchor.

[0057] like Figure 5As shown, stable structural anchors are extracted from the infrared image sequence to be compensated. Stable structural anchors can be structural regions with relatively stable positions, relatively consistent structural morphology, and controllable thermal response changes in continuous temporal phases, such as the target center, stable segments of the target contour, fixed edges of the background, horizon regions, building contours, or local texture regions with high temporal consistency.

[0058] The set of stable structural anchors is represented as: , in, Indicates the first A stable structural anchor, This indicates the number of stable structural anchors, and the stability score of the stable structural anchors is expressed as: , in, Indicates the first Stability score of each candidate structural anchor. Indicates timing consistency. Indicates edge stability. Indicates local sharpness. Indicates drift intensity. Indicates the flicker intensity. This represents the weighting coefficient. When a candidate structural anchor has high temporal consistency, high edge stability, high local clarity, low drift intensity, and low scintillation intensity, its stability score is high and can be used as a structural reference in the subsequent compensation process.

[0059] Step 5: Establish path consistency constraints.

[0060] like Figure 6 As shown, path consistency constraints are established based on stable structural anchors. Infrared turbulence can cause local drift and radiation fluctuations in the same structure over continuous time phases, but these changes usually have a certain temporal continuity. If this continuity is ignored during the compensation process, structural jumps, edge ghosting, or local misalignment can easily occur. Therefore, this embodiment constrains the drift path, radiation change path, and structural holding path of the same stable structural anchor over continuous time phases.

[0061] The path consistency constraint is represented as: , in, Indicates the first The stable structural anchor is at the first Path state in each phase This represents the path state prediction function. Path states can include one or more of the following: spatial location, local displacement, radiation response, and structural description. This constraint allows for smoother and more traceable changes in the same structure over continuous time phases.

[0062] Step 6: Layered compensation of infrared turbulence images.

[0063] like Figure 7 As shown, layered compensation is performed on the infrared image to be compensated based on the local drift field, radiation scintillation field, turbulent state unit, and path consistency constraints. The layered compensation includes local drift compensation, radiation scintillation correction, detail degradation compensation, and thermal radiation consistency preservation.

[0064] Local drift compensation is used to correct the spatial position of different regions in an infrared image based on the local drift field, reducing target contour jitter, edge drift, and local structural distortion. Radiation scintillation correction is used to correct local thermal response fluctuations based on the radiation scintillation field, reducing inconsistent grayscale levels and unstable thermal response. Detail degradation compensation is used to compensate for edge blurring and high-frequency detail loss based on turbulent state cells. Thermal radiation consistency preservation is used to maintain the relative thermal radiation relationship between the target area and the background area.

[0065] The compensated infrared image is represented as follows: , in, The compensation function can be a rule-based compensation function, an image reconstruction function, a deep network compensation function, or a combination of multiple compensation methods. The thermal radiation consistency constraint is expressed as follows: , in, This represents the thermal radiation consistency constraint term. Indicates the area to be compensated. This represents the thermal radiation structure characterization function. This represents the compensated infrared image. This constraint prevents the compensation process from altering the relative thermal radiation between the target and the background in pursuit of visual clarity.

[0066] Step 7: Output the compensation result together with the turbulence state.

[0067] like Figure 8 As shown, the output is the compensated infrared image, along with the turbulence intensity level, structural stability, thermal radiation retention, and compensation confidence level. The compensation confidence level describes the reliability of the current compensation result and can be used by subsequent target detection, identification, tracking, or monitoring systems. The compensation confidence level is expressed as: , in, Indicates the first The reliability of the compensation results for each phase. Indicates the reverse reliability corresponding to the turbulence intensity level. Indicates structural stability. Indicates the degree of heat radiation retention. Indicates the degree of residual disturbance. This represents the weighting coefficient.

[0068] Through the above steps, this embodiment can separate and estimate local spatial drift and radiation scintillation when infrared images are affected by atmospheric turbulence, and complete hierarchical compensation by combining turbulent state units, stable structural anchors and path consistency constraints, thereby improving the structural stability, thermal radiation retention and subsequent sensing availability of infrared images.

[0069] Example 2: This embodiment uses the monitoring task of transport vehicles affected by atmospheric turbulence during long-distance infrared observation in a port setting as an example to illustrate the specific implementation process of the method of the present invention.

[0070] Step 1, as follows Figure 2 As shown, a long-wave infrared imaging device was used to continuously observe the port area. The infrared imaging device operates in the 8μm–14μm band, with an image resolution of 640×512, a lens focal length of 200mm, a sampling frequency of 25fps, and an observation distance of approximately 3.5km. Infrared images at 20 different time points were continuously acquired, forming a time-series infrared image sequence. .

[0071] The image sequence is then subjected to time synchronization, spatial coarse registration, and radiometric normalization to obtain the infrared image sequence to be compensated.

[0072] Step 2, as follows Figure 3 As shown, the local drift field and radiation scintillation field are separated in the infrared image sequence to be compensated.

[0073] First, a local drift field is constructed based on the changes in edge positions and local structural displacements between adjacent temporal images: , This embodiment uses a local displacement estimation method to calculate the drift field response. Statistically, the average drift amplitude in the target region is approximately 2.8 pixels, and the average drift amplitude in the background region is approximately 1.1 pixels.

[0074] Then, a radiative scintillation field is constructed based on the compensated grayscale changes at the corresponding locations: , The calculated mean scintillation intensity of the target area is 0.17, and the mean scintillation intensity of the background area is 0.08.

[0075] Step 3, as follows Figure 4As shown, turbulent state units are constructed based on the local drift field and radiation scintillation field obtained in step 2. This embodiment uses three scales—32×32, 64×64, and 128×128—to divide the image into regions. For the k-th region, turbulent state units are constructed at the l-th scale: , Drift intensity, scintillation intensity, edge stability, local sharpness, and temporal consistency are all normalized to the [0,1] interval. Further calculation of the turbulence state score is then performed. , In this embodiment, the following is taken: .

[0076] Calculations show that approximately 18% of the area in the image is under high turbulence, 47% is under moderate turbulence, and the remaining area is under low turbulence. In this embodiment, Figure 9 This represents a heatmap showing the turbulence state score and the results of dividing the region into different levels.

[0077] In step 4, such as Figure 5 As shown, stable structural anchors are extracted from continuous temporal images. A set of stable structural anchors is obtained by comprehensively analyzing the temporal consistency, edge stability, and local sharpness of the regions. , This embodiment extracts a total of 42 stable structural anchors, including building edges, dock outlines, road boundaries, and target stable thermal structural areas. The stability score of each stable structural anchor is then calculated. , In this embodiment, the following is taken: Ultimately, structural anchors with a stability score greater than 0.75 were retained as reference structures in the subsequent compensation process.

[0078] In step 5, such as Figure 6 As shown, path consistency constraints are established based on stable structural anchors. For the j-th stable structural anchor in the t-th time phase: .

[0079] Further establish path consistency constraints: , In this embodiment, a second-order temporal prediction method is used to construct the path prediction function. After constraints, the structural position fluctuation between consecutive time phases is reduced by approximately 41%, and local ghosting phenomena are significantly reduced.

[0080] In step 6, such as Figure 7As shown, the infrared image is layered and compensated based on the local drift field, radiation scintillation field, turbulent state unit, stable structure anchor, and path consistency constraint.

[0081] The compensated infrared image is represented as follows: , Local drift compensation is used to correct spatial position offset, radiation scintillation correction is used to reduce thermal response fluctuations, and detail degradation compensation is used to restore target edges and local texture structure. Simultaneously, thermal radiation consistency constraints are established. , After compensation, the sharpness of the target edge is improved by about 36%, the structural stability is improved by about 42%, and the relative thermal radiation relationship between the target and the background remains stable.

[0082] In step 7, such as Figure 8 As shown, the compensation result and turbulence state are jointly output. The compensated infrared image is output, along with the turbulence intensity level, structural stability, thermal radiation retention, and compensation confidence level. The compensation confidence level is calculated using the following formula: .

[0083] In this embodiment, the following is taken: Ultimately, the following was obtained: Turbulence intensity level: Moderate; Structural stability: 0.87; Thermal radiation retention: 0.91; Compensation reliability: 0.89.

[0084] The results show that the method of the present invention can effectively separate spatial drift disturbances and radiation scintillation disturbances in infrared turbulence, and improve image structure stability and visual clarity while maintaining thermal radiation relationship, thereby providing more reliable infrared image input for subsequent target detection, target recognition and status monitoring.

Claims

1. A method for compensating infrared turbulence images based on the separation of local drift field and radiative scintillation field, characterized in that, Includes the following steps: Step 1: Obtain the time-series infrared image sequence affected by atmospheric turbulence, and perform time synchronization, spatial coarse registration and radiometric normalization processing to obtain the infrared image sequence to be compensated. The time-series infrared image sequence is represented as follows: ， in, Indicates the first Infrared images of each phase; Indicates the number of phases; Step 2: Based on the local displacement changes, edge position changes, and local grayscale fluctuation changes between adjacent time phases in the infrared image sequence to be compensated, construct the local drift field and the radiation scintillation field respectively; In step 2, the local drift field and the radiation scintillation field are respectively represented as: ， ， in, Indicates the first Spatial position of each phase Local drift field response at the location; Indicates the first Spatial position of each phase The radiative scintillation field response at the location; Represents the local drift estimation function; Represents the radiative scintillation estimation function; Step 3: Construct turbulent state units based on local drift field, radiative scintillation field, edge stability, local clarity, and temporal consistency; Step 4: Extract stable structural anchors from the infrared image sequence to be compensated; Step 5: Establish path consistency constraints based on stable structural anchors; Step 6: Based on the local drift field, radiation scintillation field, turbulent state unit and path consistency constraint, perform layered compensation on the infrared image to be compensated to obtain the compensated infrared image. Step 7: Output the compensated infrared image and the corresponding turbulence state information.

2. The infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field as described in claim 1, characterized in that, In step 2, the local drift field is used to characterize the spatial displacement change of the same local structure in continuous time phases; the local drift intensity is expressed as: ， in, Indicates spatial location The local displacement vector between adjacent time phases; The radiative scintillation field is used to characterize thermal response fluctuations in a continuous time phase, and its scintillation intensity is expressed as: ， in, This represents the position compensation amount obtained based on the local drift field.

3. The infrared turbulence image compensation method based on the separation of local drift field and radiative scintillation field as described in claim 1, characterized in that, In step 3, the turbulent state unit is represented as: ， in, Indicates drift intensity; Indicates the intensity of the flash; Indicates edge stability; Indicates local sharpness; Indicates temporal consistency; turbulent state score is represented as ， in, This represents the weighting coefficient.

4. The infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field as described in claim 1, characterized in that, In step 4 Regions exhibiting positional stability, structural consistency, and thermal response stability within a continuous temporal phase are extracted from the infrared image sequence to be compensated, serving as stable structural anchors; the set of stable structural anchors is represented as follows: ， in, Indicates the first A stable structural anchor; Indicates the number of anchors in the stable structure; The stability score is expressed as follows: ， in, This represents the weighting coefficient.

5. The infrared turbulence image compensation method based on the separation of local drift field and radiative scintillation field as described in claim 1, characterized in that, In step 5, path consistency constraints are established based on stable structural anchors: ， in, This represents the path consistency constraint. Indicates the first The stable structural anchor is at the first Path state in each time phase; This represents the path state prediction function.

6. The infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field as described in claim 1, characterized in that, The hierarchical compensation in step 6 includes: Local drift compensation, radiation scintillation correction, detail degradation compensation, and preservation of thermal radiation uniformity; the compensated infrared image is represented as follows: ， in, This represents the compensated infrared image; This represents the infrared turbulence image compensation function.

7. The infrared turbulence image compensation method based on the separation of local drift field and radiative scintillation field as described in claim 6, characterized in that, The requirement for maintaining thermal radiation consistency is met: ， in, This represents the thermal radiation consistency constraint term; Indicates the area to be compensated; This represents the thermal radiation structure characterization function.

8. The infrared turbulence image compensation method based on the separation of local drift field and radiation scintillation field as described in claim 1, characterized in that, In step 7 The credibility of compensation is expressed as: ， in, Indicates the first The reliability of compensation results for each phase; This indicates the reverse reliability corresponding to the turbulence intensity level. Indicates structural stability; Indicates the degree of heat radiation retention; Indicates the degree of residual disturbance; This represents the weighting coefficient.

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