Image degradation simulation method for atmospheric turbulence space-time blurring and aerosol multiple scattering

Abstract

The invention relates to the technical field of atmosphere image degradation simulation, in particular to an image degradation simulation method for atmosphere turbulence space-time blurring and aerosol multiple scattering, and the method comprises the steps: obtaining an atmosphere turbulence data set, and generating a three-dimensional space-time refractive index fluctuation field; constructing an atmospheric turbulence influence operator, and respectively constructing a real-time turbulence influence operator and an atmospheric turbulence influence distortion operator; constructing a multi-scattering influence operator, obtaining aerosol scattering parameters, and simulating a multi-scattering process through Monte Carlo ray tracing; fusing the atmospheric turbulence influence operator and the multiple scattering influence operator to form a composite atmospheric degradation model; and inputting the clear image into the composite atmospheric degradation model, and outputting a degraded image. According to the method, the Monte Carlo multiple scattering simulation and the dynamic turbulence operator are fused, the complex atmosphere comprehensive degradation effect can be efficiently and vividly reproduced, and continuous degradation processing of the video frame can be carried out due to the fact that the turbulence operator has space-time continuity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of atmospheric image degradation simulation, in particular to an image degradation simulation method based on atmospheric turbulence space-time blur and aerosol multiple scattering. BACKGROUND

[0002] With the rapid development of computer vision technology, outdoor vision systems are increasingly applied in outdoor scenes such as remote sensing monitoring, automatic driving, security monitoring, etc. The performance of these systems in open and uncertain environments is significantly affected by atmospheric factors. These influences can greatly reduce image quality, distort spatial information, and reduce the accuracy of target detection and recognition algorithms. Among them, atmospheric turbulence can cause light wave front distortion, resulting in geometric distortion and resolution degradation of the image; and aerosols, haze, fog and other suspended particles in the atmosphere can cause multiple scattering of light, resulting in image degradation effects such as distortion and blur. In dense fog, the effective range of a visible light camera can be drastically reduced to a few meters, severely limiting the perception ability of long-distance monitoring and automatic driving.

[0003] Existing image degradation models mostly focus on simulating a single factor or performing the same blurring on images at different times, making it difficult to continuously represent the influence of the coupling of multiple physical fields of the atmosphere at the physical level. Therefore, the generated degraded images deviate from the physical mechanism of the images captured in the real atmospheric environment, thereby limiting the application of related image restoration and enhancement algorithms in real scenes. SUMMARY

[0004] To solve the technical problems existing in the prior art, the embodiments of the present application provide an image degradation simulation method based on atmospheric turbulence space-time blur and aerosol multiple scattering, which can obtain a degraded image that accurately reflects the combined atmospheric effect, provide effective data support for the research and testing of computer vision algorithms that eliminate the influence of atmospheric turbulence, and effectively overcome the bottleneck of difficult real data acquisition. The technical solution is as follows: The embodiments of the present application provide an image degradation simulation method based on atmospheric turbulence space-time blur and aerosol multiple scattering, which comprises: S1, acquiring an atmospheric turbulence dataset to generate a three-dimensional space-time refractive index fluctuation field; S2, constructing an atmospheric turbulence influence operator, respectively constructing a real-time turbulence influence operator and an atmospheric turbulence influence distortion operator; S3, constructing a multiple scattering influence operator, acquiring aerosol scattering parameters, and simulating the multiple scattering process through Monte Carlo ray tracing; S4, fusing the atmospheric turbulence influence operator and the multiple scattering influence operator to form a combined atmospheric degradation model; S5, inputting a clear image into the combined atmospheric degradation model to output a degraded image.

[0005] Preferably, in S1, the atmospheric turbulence dataset is obtained, and a three-dimensional space-time refractive index fluctuation field is generated, including obtaining an atmospheric turbulence dataset composed of key parameters from an open source dataset, and based on the atmospheric turbulence dataset, a three-dimensional space-time refractive index fluctuation field capable of representing the characteristics of the random fluctuation of atmospheric refractive index is obtained.

[0006] Preferably, in S2, a real-time turbulence influence operator is constructed, including using the three-dimensional space-time refractive index fluctuation field to calculate a real-time turbulence influence operator of the intensity scintillation of the light wave front caused by atmospheric turbulence at different times through a step-by-step Fourier method.

[0007] Preferably, in S2, an atmospheric turbulence influence distortion operator is constructed, including: based on input atmospheric parameters such as refractive index structure constant, turbulence inner and outer scale, system aperture and focal length, path length, wavelength, pixel physical size, image resolution, calculating the angle of arrival variance and the covariance function describing the correlation between pixels, then converting the white noise vector field into a geometric distortion field with the required correlation through the method of frequency domain filtering, and finally obtaining the displacement matrix of the pixels.

[0008] Preferably, in S3, aerosol scattering parameters are obtained, and a Monte Carlo ray tracing is used to simulate the multiple scattering process, including obtaining the key particle parameters of the main scattering medium, obtaining the extinction coefficient, scattering coefficient, absorption coefficient and scattering phase function of various particles based on the OPAC model, so as to obtain the light intensity distribution probability under different scattering angles, using the scattering phase function as the core input, using the Monte Carlo ray tracing technology to simulate the random walk process of a large number of photons in the atmospheric channel, and statistically analyzing the space-time distribution of the photons after multiple scattering, and then calculating the multiple scattering influence operator representing the pulse broadening and energy attenuation caused by the multiple scattering effect.

[0009] Preferably, in S4, the fusion manner of the atmospheric turbulence influence operator and the multiple scattering influence operator is convolution operation.

[0010] Preferably, the output degraded image is an atmospheric degraded image containing geometric distortion and blurring effect, which is suitable for dynamic video sequence processing.

[0011] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects: 1. The present application can efficiently and realistically reproduce complex atmospheric comprehensive degradation effects by fusing Monte Carlo multiple scattering simulation and dynamic turbulence operator, and can also perform continuous degradation processing of video frames due to the space-time continuity of the turbulence operator. It can be widely applied in image restoration algorithm test, remote sensing image quality analysis and environmental monitoring fields, and can provide reliable atmospheric simulation data support for automatic driving, earth observation and other key applications.

[0012] 2、The method fuses the atmospheric turbulence influence and the multiple scattering influence two degradation modes, has better image fidelity and physical consistency. The atmospheric degradation image generated by the method can more accurately coincide with the observation result under the real turbulence and scattering environment, reproduces the geometric distortion caused by turbulence, the flicker effect and the global blur caused by multiple scattering, and significantly enhances the physical reality and scene depth of the degradation image. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 The figure shows the image degradation simulation method flow chart of atmospheric turbulence space-time blur and aerosol multiple scattering according to the embodiment of the present application. DETAILED DESCRIPTION

[0015] The technical solutions in the present application will be described below in combination with the drawings.

[0016] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0017] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0018] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. When the distinction is not emphasized, the meanings expressed are consistent.

[0019] In order to make the technical problems, technical solutions and advantages of the present application more clear, the following will be described in detail in combination with the drawings and specific embodiments.

[0020] The embodiment of the present application provides an image degradation simulation method for atmospheric turbulence space-time blur and aerosol multiple scattering, as shown in the following formula (1) and formula (2), the method comprises the following steps: Figure 1 S1, acquiring an atmospheric turbulence data set to generate a three-dimensional space-time refractive index fluctuation field; The atmospheric turbulence data set includes key parameters such as atmospheric pressure, temperature and other key parameters in the actual scene, which are obtained from an open source data set (such as JHTDB database) to form an atmospheric turbulence data set.

[0021] Based on the above atmospheric turbulence data set, a three-dimensional space-time refractive index fluctuation field capable of representing the random fluctuation characteristics of atmospheric refractive index is obtained. Specifically, the refractive index values of each node on the three-dimensional grid are calculated by using the modified Edlén formula shown in formula (1) and formula (2), and an interpolation method is used to obtain a three-dimensional space-time fluctuation field. (1); In formula (1), : the refractive index of standard air (Refractivity of standard air); the temperature t is 20 DEG C (ITS-90) under standard conditions, the pressure p is 100,000 Pa, and the carbon dioxide content x is 0.04%; n: the refractive index; σ=1 / λ vac ,λ vac : vacuum wave number, unit μm -1 .

[0022] (2); In formula (2), : the refractive index of dry air containing CO2 under actual temperature t and pressure p. p: the actual atmospheric pressure, unit Pa. t: the actual air temperature, unit DEG C, based on ITS-90 temperature scale.

[0023] The refractive index n(x, y, z) of the entire three-dimensional space grid is calculated by formula (1) and formula (2).

[0024] S2, constructing an atmospheric turbulence influence operator, specifically, constructing a real-time turbulence influence operator and an atmospheric turbulence influence distortion operator.

[0025] Constructing a real-time turbulence influence operator: including using the three-dimensional space-time refractive index fluctuation field, calculating the real-time turbulence influence operator of the intensity scintillation of the light wave front caused by the atmospheric turbulence at different times by step-by-step Fourier method.

[0026] ​Specifically, wave propagation simulation: set the propagation path of the light wave (e.g. horizontal path or oblique path). Take the series of refractive index fluctuation field generated in step 1 as the transmission medium. Use the step-by-step Fourier method to simulate the propagation process of the light wave through the time-varying fluctuation field.

[0027] Set the initial complex amplitude of the emitted light beam at z = 0. x, y represent the two-dimensional light field plane at z j .

[0028] a. Diffractive propagation: Solve the diffraction of the light wave in the uniform medium Δz, transform it to the frequency domain, apply the diffraction propagation operator H, and then inverse transform it back to the spatial domain.

[0029] (3); F represents the Fourier transform on a two-dimensional plane, z j-1 the field function on the plane, The two-dimensional Fourier transform formula of the field function on the plane is formula (4), which converts the light field in the spatial domain to the frequency domain: (4); In formula (4), represents the spatial domain light field at the j-1th step; represents the frequency domain light field at the j-1th step; and represent the spatial frequencies along the x and y directions, x, y represent the spatial coordinates; is the kernel function of the Fourier transform.

[0030] represents the inverse Fourier transform, where z = z j-1 The two-dimensional Fourier transform formula of the field function on the plane converts the light field in the spatial domain to the frequency domain: Convert the light field in the frequency domain to the spatial domain; (5); In formula (5), represents the degenerate light field at the jth step.

[0031] The H operator is the solution of the light field propagation equation in the frequency domain in a uniform medium: (6); In formula (6), , is the frequency domain coordinate; represents the vacuum beam: ; b. Phase modulation: Calculate the additional phase delay introduced by the refractive index fluctuation of the medium from j-1 to j to the light wave:​ (7); In formula (7), represents the phase delay caused by the refractive index fluctuation of the step length Δz: (8); In formula (8), represents the refractive index fluctuation of each node, represents the mean refractive index of the three-dimensional refractive index fluctuation field.

[0032] (9); c. Iteration: From j=0 (light source) to j=Z (receiver): .

[0033] d. Final result: The light field received by the receiver is U(x, y, Z), and the light intensity is: (10); Finally, the light intensity distribution at each moment is obtained at the receiving end, and the light intensity distribution at the receiving end is defined as a real-time turbulence influence operator.

[0034] Constructing the atmospheric turbulence influence distortion operator: including: based on the input atmospheric parameters such as refractive index structure constant, turbulence inner and outer scale, system focal length, path length, the covariance function describing the correlation between pixels is calculated, then the white noise vector field is converted into a geometric distortion field with the required correlation through the method of frequency domain filtering, and finally the pixel displacement matrix is obtained.

[0035] Specifically, for the image deformation caused by turbulence, a two-dimensional random deformation vector field based on physics is used to calculate the pixel displacement matrix, and the pixel displacement matrix is calculated as follows: (11); In formula (11), , is a vector field of 2x1 vector of random Gaussian white noise for each element.

[0036] (12); In formula (12), and are given by .

[0037] (13); (14); The covariance matrix C(v) defines the correlation between pixels, where is the pixel spacing, is a unit directional vector; From the theory of turbulence: (15); Calculate Calculate with minus sign Calculate with plus sign, κ is a dimensionless integral variable, contained in the real non-negative number; is the refractive index structure constant; J0 and J2 are the first kind of Bessel functions; (16); (17); In formula (17), ξ represents a dimensionless parameter range of 0≤ξ≤1; represents the normalized turbulent outer scale; represents the normalized observation angle; represents the aperture angle size.

[0038] S3, construct a multiple scattering influence operator, obtain aerosol scattering parameters, and simulate the multiple scattering process by Monte Carlo ray tracing; Obtaining aerosol scattering parameters includes obtaining key particle parameters of the main scattering medium, obtaining extinction coefficients, scattering coefficients, absorption coefficients and scattering phase functions of various particles based on the OPAC model, so as to obtain the light intensity distribution probability under different scattering angles.

[0039] Obtaining key particle parameters and scattering phase functions of the main scattering medium: using OPAC model to select multiple particles to obtain their anisotropy factor g, extinction coefficient σ e , absorption coefficient σ s , scattering coefficient σ a .

[0040] The Henyey-Greenstein (HG) scattering phase function is as formula (18): (18); In formula (18), θ represents the scattering angle calculated by the scattering phase function; represents a random number between 0 and 1.

[0041] Taking the scattering phase function as the core input, using the Monte Carlo ray tracing technology, simulating the random walk process of a large number of photons in the atmospheric channel, and counting the space-time distribution of the photons after multiple scattering, and then calculating the multiple scattering influence operator representing the pulse broadening and energy attenuation caused by multiple scattering effects.

[0042] Monte Carlo simulation settings: Light source modeling: model the light source at the transmitting end as a large number (for example, 107 -10 9 Each photon carries initial energy and direction (perpendicular to the emitting aperture).

[0043] Transmission medium modeling: According to the parameters obtained in step S3, set the extinction coefficient σ e , scattering coefficient σ s and absorption coefficient σ a , and scattering phase function P(θ) of the atmospheric channel.

[0044] Photon tracing: simulate the random walk of each photon in the medium: a. Free path: according to σ e , randomly generate the transmission distance of the next scattering or absorption event of the photon.

[0045] b. Scattering direction: when scattering occurs, the new propagation direction of the photon is randomly determined according to the scattering phase function P(θ) and the azimuth angle distributed uniformly.

[0046] c. Energy attenuation: after each interaction, the weight (energy) of the photon is attenuated by the single scattering albedo σ s / σ e .

[0047] d. Termination condition: when the photon is absorbed (weight below threshold), escapes the channel boundary or reaches the receiving end, stop tracing.

[0048] Statistics and operator generation: At the receiving end plane (set a certain size and position), record the spatial coordinates (x, y) of all arriving photons, arrival time t (delay relative to direct light) and remaining energy w, and integrate them.

[0049] Discretize the receiving plane into a pixel grid, and statistically obtain the results of the energy and arrival time of all photons in each pixel.

[0050] The multiple scattering influence operator is defined as the spatial and temporal distribution of photon energy at the receiving end, i.e. the point spread function (PSF). It directly reflects the effects of spatial blurring (spot diffusion) and temporal broadening (pulse delay) caused by multiple scattering.

[0051] S4, fuse the atmospheric turbulence influence operator and the multiple scattering influence operator to form a composite atmospheric degradation model; The fusion manner of the atmospheric turbulence influence operator and the multiple scattering influence operator is convolution operation. Specifically, the atmospheric turbulence influence operator obtained in the step S2 and the multiple scattering influence operator obtained in the step S3 are included in a unified transmission model, based on the light wave transmission theory, the convolution operation is adopted to calculate a comprehensive influence model capable of comprehensively reflecting the combined action mechanism of atmospheric turbulence (wavefront distortion) and multiple scattering (spatial and temporal dispersion), that is, a composite atmospheric degradation model containing a PSF and a geometric distortion, and the composite atmospheric degradation model is as follows: (19); (20).

[0052] In the formula (20), is a real-time turbulence influence operator; is a multiple scattering influence operator; is an operator fused with real-time turbulence influence and multiple scattering influence.

[0053] S5, input the clear image into the composite atmospheric degradation model, and output a degraded image.

[0054] The output degraded image is an atmospheric degradation image containing geometric distortion and blurring effect, and is suitable for dynamic video sequence processing.

[0055] The running process of the composite atmospheric degradation model is as follows: Input clear image: prepare a high-definition digital image as input.

[0056] Geometric distortion processing: (21); In the formula (21), is an original image, indicates that the original pixel position is shifted to position, is an image after the pixel of is calculated by interpolation.

[0057] Blur processing: spatial domain convolution, for each time t, the geometric distortion image is convolved with the corresponding comprehensive PSF; or frequency domain filtering, the geometric distortion image and the PSF total are respectively subjected to Fourier transform, multiplied in the frequency domain, and then subjected to inverse Fourier transform:

[0058] ;

[0059] Output: finally output one or a series (in a dynamic scene) of atmospheric degradation images I containing geometric distortion and blurring in visual effect模糊 .

[0060] The embodiment aims to provide an atmospheric degradation image generation method based on a physical mechanism, realize high-fidelity physical degradation simulation of a clear image by coupling atmospheric turbulence and multiple scattering effects. Atmospheric parameters are input, and an atmospheric turbulence influence operator and a multiple scattering influence operator are calculated respectively, the atmospheric refractive index fluctuation field is obtained from a turbulence data set, and the real-time turbulence influence operator is obtained from the atmospheric refractive index fluctuation field; the refractive index structure constant, the turbulence inner and outer scales, the focal length, the camera aperture and the wavelength are input, the pixel influence covariance matrix is constructed, the distortion field is produced, and thus the atmospheric turbulence influence distortion operator is obtained; the OPAC model obtains scattering phase functions, extinction coefficients, scattering coefficients and absorption coefficients of multiple particles, the Monte Carlo path tracking technology is used to obtain the multiple scattering influence operator, and the real-time turbulence influence operator, the atmospheric turbulence influence distortion operator and the multiple scattering influence operator calculated simultaneously are input into a composite atmospheric degradation model, and a degraded image is obtained.

[0061] The above embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0062] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the front and rear associated objects are in an "or" relationship, but can also represent an "and / or" relationship, which can be understood according to the context.

[0063] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including a single item or any combination of multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0064] It should be understood that the size of the sequence number of the above-mentioned processes does not mean the order of execution in various embodiments of the present application. The execution order of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-mentioned devices, apparatuses and units can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0067] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0068] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0069] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0070] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0071] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An image degradation simulation method for atmospheric turbulence spatio-temporal blurring and aerosol multiple scattering, characterized in that, The method comprises: S1, acquiring an atmospheric turbulence dataset to generate a three-dimensional space-time refractive index fluctuation field; S2, constructing an atmospheric turbulence influence operator, respectively constructing a real-time turbulence influence operator and an atmospheric turbulence influence distortion operator; S3, constructing a multiple scattering influence operator, acquiring aerosol scattering parameters, and simulating the multiple scattering process through Monte Carlo ray tracing; S4, fusing the atmospheric turbulence influence operator and the multiple scattering influence operator to form a composite atmospheric degradation model; S5, inputting a clear image into the composite atmospheric degradation model to output a degraded image.

2. The method of claim 1, wherein the method is characterized by, In S1, the three-dimensional space-time refractive index fluctuation field is generated by acquiring an atmospheric turbulence dataset, including: acquiring an atmospheric turbulence dataset composed of key parameters from an open source dataset, and obtaining a three-dimensional space-time refractive index fluctuation field capable of representing the random fluctuation characteristics of atmospheric refractive index based on the atmospheric turbulence dataset.

3. The method of claim 1, wherein the method is characterized by, In S2, the real-time turbulence influence operator is constructed, including: using the three-dimensional space-time refractive index fluctuation field, calculating the real-time turbulence influence operator of the atmospheric turbulence on the light wave front intensity scintillation at different times through the step-by-step Fourier method.

4. The method of claim 1, wherein the method is characterized by, In S2, the atmospheric turbulence influence distortion operator is constructed, including: based on the input atmospheric parameters such as refractive index structure constant, turbulence inner and outer scale, system aperture and focal length, path length, wavelength, pixel physical size, image resolution, calculating the angle of arrival variance and the covariance function describing the correlation between pixels, then converting the white noise vector field into a geometric distortion field with the required correlation through the frequency domain filtering method, and finally obtaining the displacement matrix of the pixels.

5. The method of claim 1, wherein the method is characterized by: In S3, the aerosol scattering parameters obtained are the key particle parameters of the main scattering medium, and the extinction coefficient, scattering coefficient, absorption coefficient and scattering phase function of various particles are obtained based on the OPAC model, so as to obtain the light intensity distribution probability under different scattering angles, and the multiple scattering process is simulated through Monte Carlo ray tracing, including: taking the scattering phase function as the core input, using the Monte Carlo ray tracing technology to simulate the random walk process of a large number of photons in the atmospheric channel, and statistically analyzing the space-time distribution of the photons after multiple scattering to reach the receiving end, and then calculating the multiple scattering influence operator representing the light pulse broadening and energy attenuation caused by the multiple scattering effect.

6. The method of claim 1, wherein, In S4, the fusion method of the atmospheric turbulence influence operator and the multiple scattering influence operator is convolution operation.

7. The method of claim 1, wherein the method is characterized by: The output degraded image is an atmospheric degradation image containing geometric distortion and blurring effect, which is suitable for dynamic video sequence processing.

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