Ghost imaging monitoring method and system based on environmental coupling simulation and image enhancement

Abstract

The present application provides a kind of ghost imaging monitoring method and system based on environmental coupling simulation and image enhancement, the method comprises: setting initial light source, the initial light source is handled with splitter matrix model to obtain probe light path light source and reference light path light source, based on the probe light path light source and reference light path light source, respectively construct probe light path and reference light path;In probe light path, lens, object to be measured and bucket detector are set in sequence, and surface detector is set in reference light path, high gauss blur is introduced in probe light path to simulate heavy fog environment, and random phase screen based on Perlin noise is introduced to simulate turbulent environment.The present application builds ghost imaging system through Python, uses Gaussian simulation to simulate heavy fog environment and turbulent environment, and combines the concept of histogram equalization in digital image processing, the imaging quality of ghost imaging is optimized by non-linear mapping gray scale chart, and the imaging quality of ghost imaging system under long-distance condition can be improved.

Description

Technical Field

[0001] This invention relates to the field of optical imaging technology, and in particular to a ghost imaging monitoring method and system based on environmental coupling simulation and image enhancement. Background Technology

[0002] Information can be transmitted via images, which play an indispensable role in the information transmission process. Imaging technology is one of the important means of obtaining this information. Traditional imaging mainly relies on light emitted from a light source to illuminate an object, and the reflected light is received by a detector to form an image. Its imaging quality is greatly affected by factors such as the brightness of the light source, the reflectivity of the object, the detection accuracy, and the environment.

[0003] Although traditional imaging technology is now very mature, it has certain limitations in complex environments. For example, in mid- to long-range foggy or turbulent conditions, the resolution of traditional imaging technology is not only limited by the optical diffraction limit but also affected by scattering from water droplets in fog and turbulence, causing light to deflect and significantly reducing visibility. Fog absorbs part of the spectrum, and turbulence reduces light intensity, thus affecting image quality, reducing image visibility, and making it difficult to distinguish the outlines and shapes of objects. In mid- to long-range imaging scenarios, fog and turbulence often coexist, frequently due to the superposition of specific meteorological and environmental conditions, resulting in a negative impact on image quality that is greater than the sum of its parts. For example, in mountainous forest fire monitoring, fog and turbulence often occur together on early autumn and winter mornings, obstructing the monitoring image.

[0004] In existing technologies, relevant research has been conducted to improve imaging capabilities under complex weather conditions. For example, patent CN119200220B discloses an "Optimization Method and System for a Large-Area Ghost Imaging Search and Rescue System in Fog Environment." Its technical solution involves building a ghost imaging system based on ZEMAX optical design software, simulating a fog environment using a Mie scattering model, and optimizing optical system parameters with the goal of minimizing the point spread function width and maximizing the depth of field, thereby achieving large-area imaging in foggy environments. This solution improves the adaptability and imaging range of ghost imaging in foggy conditions to a certain extent. However, this solution still has the following limitations:

[0005] 1. The environmental simulation is too simplistic: the model only focuses on a single fog environment and does not take into account the complex meteorological conditions of fog and turbulence coupling that often occur in actual applications. Especially in medium and long-distance monitoring scenarios, the random wavefront distortion caused by turbulence will cause superimposed interference with fog scattering.

[0006] 2. Lack of image post-processing mechanism: It relies entirely on the optimization of the optical system's own parameters to improve image quality, without introducing any digital image post-processing technology, resulting in limited ability to recover image details under extremely low contrast conditions.

[0007] Therefore, for the long-distance monitoring needs in fog and turbulence coupled environments, existing technologies have not yet provided a systematic solution that can both effectively simulate coupling interference and further improve imaging quality through image post-processing. Summary of the Invention

[0008] In view of the above situation, the main objective of this invention is to propose a ghost imaging monitoring method and system based on environmental coupling simulation and image enhancement to solve the above-mentioned technical problems.

[0009] This invention proposes a ghost imaging monitoring method based on environmental coupling simulation and image enhancement, the method comprising the following steps:

[0010] Step 1: Set the initial light source and use the beam splitter matrix model to split the initial light source into two beams to obtain the probe light source and the reference light source.

[0011] Step 2: Based on the aforementioned detection optical path light source and reference optical path light source, construct the detection optical path and reference optical path respectively; sequentially set up a lens, the object to be measured, and a barrel detector on the detection optical path, and set up a surface detector on the reference optical path;

[0012] Step 3: Introduce Gaussian blur into the probe optical path to simulate a foggy environment, and introduce a random phase screen based on Perlin noise to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference.

[0013] Step 4: Perform optical field propagation simulation based on the detection optical path and reference optical path after coupling environmental interference. In each simulation, calculate the optical field distribution at the barrel detector position and the optical field distribution at the surface detector position respectively.

[0014] Step 5: Construct a correlation function based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. Use the correlation function to inversely derive the light field distribution, and then calculate the ghost image of the object under test based on the second-order correlation function between the light field and the light intensity.

[0015] Step 6: Perform histogram equalization on the ghost image of the object under test using Python to obtain the ghost image under the coupled fog and turbulence environment.

[0016] This invention also proposes a ghost imaging monitoring system based on environment coupling simulation and image enhancement, wherein the system employs the ghost imaging monitoring method based on environment coupling simulation and image enhancement as described above, and the system includes:

[0017] Ghost imaging module, used for:

[0018] An initial light source is set, and the initial light source is split into beams using a beam splitter matrix model to obtain the probe light source and the reference light source.

[0019] Based on the aforementioned detection optical path light source and reference optical path light source, a detection optical path and a reference optical path are constructed respectively; a lens, the object to be measured, and a barrel detector are sequentially arranged on the detection optical path, and a surface detector is arranged on the reference optical path;

[0020] Gaussian blurring is introduced into the probe optical path to simulate a foggy environment, and a random phase screen based on Perlin noise is introduced to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference.

[0021] The optical field propagation simulation is performed based on the detection optical path and the reference optical path after coupling environmental interference. In each simulation, the optical field distribution at the location of the barrel detector and the location of the surface detector are calculated respectively.

[0022] A correlation function is constructed based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. The distribution of the light field is then inverted using the correlation function. Finally, the ghost image of the object under test is calculated based on the second-order correlation function between the light field and the light intensity.

[0023] The histogram equalization module is used for:

[0024] Based on Python, histogram equalization is performed on the ghost image of the object under test to obtain the ghost image under the coupled fog and turbulence environment.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1. This invention addresses the complex meteorological environment of fog and turbulence coupling by proposing a method for simulating and suppressing coupled interference, significantly improving the adaptability and robustness of ghost imaging systems in real, harsh environments. Compared to existing technologies that only simulate single fog scattering, this invention simultaneously introduces a Gaussian blur function and a random phase screen based on Perlin noise into the probe optical path, accurately simulating the scattering attenuation effect caused by fog and the random wavefront distortion effect caused by turbulence, respectively. This coupled environment simulation method is more closely aligned with the "fog-turbulence" composite interference scenarios commonly encountered in actual monitoring in mountainous and coastal areas, enabling the optimized system to recover clear object outlines and details under more complex and stringent long-distance conditions, thus solving the technical problem of drastic image quality degradation under coupled interference in existing solutions.

[0027] 2. This invention creatively combines digital image post-processing technology with the physical process of ghost imaging, enhancing image contrast and detail resolution through back-end processing, thus overcoming the limitations of simple optical optimization. Existing technologies rely entirely on front-end system parameter optimization (such as adjusting light source size and lens focal length) to improve image quality, with limited improvement in extremely low contrast environments. After reconstructing the ghost imaging image through correlation operations, this invention further introduces histogram equalization processing based on Python. Through nonlinear redistribution of image gray levels, it effectively expands the dynamic range of the image, enhances the contrast between the target and the background, and thus recovers more details that were blurred or lost in the original correlation imaging. This achieves synergistic optimization of "physical imaging + digital enhancement," resulting in a substantial improvement in overall image quality.

[0028] 3. This invention utilizes the universal Python programming platform to implement the entire process of system modeling and image processing, improving the flexibility, scalability, and deployment efficiency of the solution, making it more suitable for rapid application in diverse monitoring scenarios. The entire chain of light field propagation simulation, environmental interference modeling, correlation calculation, and image post-processing is built based on the Python platform. This approach not only lowers the technical threshold and development costs but also facilitates the integration of more complex image processing algorithms, adaptation to different detector models, and data interaction with upper-level monitoring systems. This enables the entire solution to be deployed more quickly and flexibly in various long-distance, fixed-point continuous monitoring scenarios requiring the interaction of fog and turbulence, such as forest fire prevention, border monitoring, and maritime search and rescue, demonstrating stronger engineering practical value.

[0029] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by means of embodiments of the invention. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the steps of a ghost imaging monitoring method based on environmental coupling simulation and image enhancement proposed in this invention.

[0031] Figure 2 This is a schematic diagram comparing traditional imaging with images generated by this invention under fog and turbulence coupled environments. Detailed Implementation

[0032] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0033] These and other aspects of the embodiments of the present invention will become clear from the following description and accompanying drawings. In these descriptions and drawings, some specific embodiments of the present invention are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the present invention; however, it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0034] Please see Figure 1 This embodiment provides a ghost imaging monitoring method based on environmental coupling simulation and image enhancement, the method including the following steps:

[0035] Step 1: Set the initial light source and use the beam splitter matrix model to split the initial light source into probe light source and reference light source.

[0036] In step 1, the initial light source is split using a beam splitter matrix model to obtain the probe light source and the reference light source. The following relationship exists in the corresponding process:

[0037] ;

[0038] in, Indicates the light source of the detection optical path. Indicates the reference optical path source. Indicates the initial light source. Indicates the transmission coefficient. This represents the reflection coefficient.

[0039] It should be noted that the initial light source is a circular light source that follows a first-order Gaussian distribution; the transmission coefficient and reflection coefficient satisfy... .

[0040] Step 2: Based on the probe optical path light source and the reference optical path light source, construct the probe optical path and the reference optical path respectively; set the lens, the object to be tested and the barrel detector in sequence on the probe optical path, and set the surface detector on the reference optical path.

[0041] In step 2, a lens, the object to be measured, and a barrel detector are sequentially set in the detection optical path, and a surface detector is set in the reference optical path. This includes the following sub-steps:

[0042] In the detection optical path, the lens is defined as a phase modulation function, and the following relationship exists in the corresponding process:

[0043] ;

[0044] in, Indicates the location Phase change at that point Represents the x-axis, Represents the ordinate, Indicates phase shift, Indicates wavelength. Indicates focal length;

[0045] The object to be measured is defined as a two-dimensional binary mask. The element value of the two-dimensional binary mask is 0 or 1, where 0 represents opacity and 1 represents transparency. The following relationship exists in the process:

[0046] ;

[0047] in, Represents a two-dimensional binary mask;

[0048] The function of the bucket detector is defined as integrating and summing the light intensity distribution arriving at the bucket detector plane. The corresponding relationship is as follows:

[0049] ;

[0050] in, This represents the total light intensity value. This represents the light intensity passing through the object being measured;

[0051] In the reference optical path, the function of the surface detector is defined as recording the two-dimensional intensity distribution of the light field arriving at the surface detector plane.

[0052] Step 3: Introduce Gaussian blur into the probe optical path to simulate a foggy environment, and introduce a random phase screen based on Perlin noise to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference.

[0053] In step 3, Gaussian blur is introduced into the probe optical path to simulate a foggy environment, and a random phase screen based on Perlin noise is introduced to simulate a turbulent environment. Specifically, this includes the following sub-steps:

[0054] Gaussian blurring is applied to the light source propagating between the lens and the object under test to simulate the scattering and attenuation effects of fog on light propagation. The following relationship exists in the corresponding process:

[0055] ;

[0056] in, Indicates in Gaussian function value at that location, Indicates standard deviation, Represents an exponential function;

[0057] Perlin noise modulation is applied to the light source propagating between the object under test and the barrel detector to simulate the random wavefront distortion effect caused by turbulent environment.

[0058] Step 4: Perform optical field propagation simulation based on the detection optical path and reference optical path after coupling environmental interference. In each simulation, calculate the optical field distribution at the barrel detector position and the optical field distribution at the surface detector position.

[0059] In step 4, optical field propagation simulation is performed based on the probe optical path and reference optical path after coupling environmental interference. In each simulation, the optical field distribution at the bucket detector location and the optical field distribution at the surface detector location are calculated respectively. Specifically, the optical field propagation simulation is performed based on the probe optical path after coupling environmental interference, and the optical field distribution at the bucket detector location is calculated. The data collected by the bucket detector will be used for confidence calculation in the back end. The following relationship exists in the corresponding process:

[0060] ;

[0061] in, Indicates the bucket detector in three-dimensional coordinates light field distribution Represents three-dimensional coordinates. Represents the three-dimensional space occupied by the light source. Represents the light source in three-dimensional coordinates The complex amplitude distribution, The impulse response function from the light source to the lens is represented by the following. This represents the impulse response function from the lens to the object being measured. This represents the impulse response function of the object being measured to the barrel detector. This represents the transmission function of the object being measured.

[0062] Step 5: Construct a correlation function based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. Use the correlation function to inversely derive the light field distribution, and then calculate the ghost image of the object under test based on the second-order correlation function between the light field and the light intensity.

[0063] In step 5, a correlation function is constructed based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. The distribution of the light field is then inverted using the correlation function. Finally, the ghost image of the object under test is calculated based on the second-order correlation function between the light field and the light intensity. The following relationship exists in the corresponding process:

[0064] ;

[0065] in, Represents the light field correlation function. This indicates the intensity distribution of the reference optical path. This indicates the light intensity distribution along the detection optical path. Indicates the complex amplitude of the optical field in the reference optical path. Indicates the complex amplitude of the optical field in the probe optical path. Indicates complex conjugation. This represents the average expectation.

[0066] Step 6: Perform histogram equalization on the ghost image of the object under test using Python to obtain the ghost image under the coupled fog and turbulence environment.

[0067] In step 6, histogram equalization is performed on the ghost image of the object under test using Python to obtain the ghost image under the coupled fog and turbulence environment. The following relationship exists in the corresponding process:

[0068] ;

[0069] in, This represents the grayscale value of a pixel in the ghost image of the object under test. Indicates to Perform grayscale swapping. Indicates the grayscale depth. Indicates the number of pixels. The histogram distribution represents the ghost image of the object under test.

[0070] It should be noted that, based on traditional histogram equalization, this invention proposes an adaptive histogram equalization specifically for ghost imaging, utilizing the additional information from ghost imaging to guide image enhancement. Confidence is estimated by calculating the correlation between the illumination pattern corresponding to image pixels and the bucket detector signal. Then, different histogram equalization methods are applied to each region based on its varying confidence level. For high-confidence regions, we use adaptive histogram equalization; for medium-confidence regions, we use contrast-limited adaptive histogram equalization; and for low-confidence regions, we use a conventional histogram equalization algorithm.

[0071] Contrast-limited adaptive histogram equalization controls the contrast enhancement by limiting the height of local histograms, effectively suppressing noise amplification; while adaptive histogram equalization performs histogram equalization independently in local areas, which can improve details but is prone to over-amplifying noise.

[0072] This embodiment also provides a ghost imaging monitoring system based on environmental coupling simulation and image enhancement, wherein the system applies the ghost imaging monitoring method based on environmental coupling simulation and image enhancement as described above, and the system includes:

[0073] Ghost imaging module, used for:

[0074] An initial light source is set, and the initial light source is split into beams using a beam splitter matrix model to obtain the probe light source and the reference light source.

[0075] Based on the aforementioned detection optical path light source and reference optical path light source, a detection optical path and a reference optical path are constructed respectively; a lens, the object to be measured, and a barrel detector are sequentially arranged on the detection optical path, and a surface detector is arranged on the reference optical path;

[0076] Gaussian blurring is introduced into the probe optical path to simulate a foggy environment, and a random phase screen based on Perlin noise is introduced to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference.

[0077] The optical field propagation simulation is performed based on the detection optical path and the reference optical path after coupling environmental interference. In each simulation, the optical field distribution at the location of the barrel detector and the location of the surface detector are calculated respectively.

[0078] A correlation function is constructed based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. The distribution of the light field is then inverted using the correlation function. Finally, the ghost image of the object under test is calculated based on the second-order correlation function between the light field and the light intensity.

[0079] The histogram equalization module is used for:

[0080] Based on Python, histogram equalization is performed on the ghost image of the object under test to obtain the ghost image under the coupled fog and turbulence environment.

[0081] To verify the effectiveness of this invention, a transmissive object "G", an "apple", and a "human figure" were used as examples to be tested. The specific steps are as follows:

[0082] S1. Select a suitable light source:

[0083] In ghost imaging, the selected light source must be able to generate a random speckle distribution. This invention selects a circular light source with a first-order Gaussian distribution. A beam splitter with no polarization characteristics is placed behind the light source, splitting it into two beams with the same distribution characteristics. One beam is directed towards the reference optical path, and the other towards the probe optical path. In the numerical simulation, this invention sets up... The visibility is 2km, and the image of the object under test has a resolution of 64×64 pixels.

[0084] S2. Establish the reference optical path and the probe optical path:

[0085] Estimate the location range of the object to be measured, and input appropriate light source size, lens, and aperture size to construct the optical path. Set the approximate location of the target to a distance of 2km, and set the light source size to 1×1, the lens focal length to 100mm, and the aperture size to 10mm. Set the detector of the reference optical path to a 64×64 pixel area detector for data acquisition, and the detector of the detection optical path only has the function of recording the total light intensity.

[0086] S3. Fog and turbulence simulations using Gaussian blur and Perlin noise:

[0087] A Gaussian blur, simulating fog, is applied to the light source, lens, and object under test. Perlin noise is used between the object under test and the barrel detector to simulate turbulence. Gaussian blur accurately describes the propagation characteristics of light in foggy environments, including attenuation, scattering, and polarization. Perlin noise accurately describes the propagation characteristics of light under turbulence, including flickering, drift, and intensity fluctuations. Comparison with measured data shows that Gaussian blur and Perlin noise yield results very close to reality. Therefore, this invention selects Gaussian blur and Perlin noise to simulate complex environments, achieving the desired effect by adjusting their parameters.

[0088] S4. Set the target to be tested:

[0089] This example uses a transmissive object "G", an "apple", and a "human figure" as the target to be tested. The image and address to be tested are input into Python, and the visibility is 2km to simulate long-distance ghost imaging under a fog and turbulence coupled environment.

[0090] S5. Calculate the data returned by the detector and generate an image:

[0091] The main function for simulating long-range ghost imaging under a fog-turbulence coupled environment is run. The system first clears the data of the detector and the barrel detector. Then, the two detectors run independently, save the data of each run, and return it. After 1500 cycles, the information of the object is obtained through correlation calculation, and the long-range image of the object under the fog-turbulence coupled environment is generated through visualization operation.

[0092] S6. The confidence level is obtained by calculating the correlation between the data returned by the bucket detector and the pixel illumination pattern of the ghost image. Due to the different confidence levels, different histogram equalization methods will be applied in different regions. The following relationship exists in the corresponding process:

[0093] ;

[0094] By using the histogram equalization formula shown above and performing histogram equalization on the ghost image returned with the set parameters, the optimized ghost image can be returned.

[0095] Please see Figure 2 To verify the advantages of ghost imaging under histogram equalization in fog and turbulence coupled environments compared to traditional imaging, Figure 2 The imaging quality of this invention was compared with that of conventional imaging, wherein, Figure 2 In this context, 'a' represents a traditionally imaged image. Figure 2 In this context, b represents the image of the ghost image. Figure 2 In this context, 'c' represents the histogram-equalized ghost image. From... Figure 2 It can be seen that in a fog and turbulence coupled environment with a visibility of 2km, where traditional imaging is basically distorted, ghost imaging can still recover the information of the object based on the collected light field energy. With the help of histogram equalization, the image quality of ghost imaging can be significantly improved.

[0096] from Figure 2 As can be seen, compared with traditional surveillance imaging, the method of this invention has the following significant advantages: First, it has a significant advantage in image clarity. Ghost imaging has strong anti-interference capabilities. This invention combines Gaussian blur, Perlin noise, and the concept of histogram equalization in traditional digital image processing. By using Gaussian blur and Perlin noise to simulate the propagation characteristics of light attenuation, scattering, and polarization in fog and turbulence coupled environments, it closely resembles reality and can be used for long-distance fire monitoring of forests in fog and turbulence coupled environments. This technology is feasible both in principle and experimentally, and has already been verified in the laboratory.

[0097] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0098] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0099] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0100] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A ghost imaging monitoring method based on environmental coupling simulation and image enhancement, characterized in that, The method includes the following steps: Step 1: Set the initial light source and use the beam splitter matrix model to split the initial light source into two beams to obtain the probe light source and the reference light source. Step 2: Based on the aforementioned detection optical path light source and reference optical path light source, construct the detection optical path and reference optical path respectively; sequentially set up a lens, the object to be measured, and a barrel detector on the detection optical path, and set up a surface detector on the reference optical path; Step 3: Introduce Gaussian blur into the probe optical path to simulate a foggy environment, and introduce a random phase screen based on Perlin noise to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference. Step 4: Perform optical field propagation simulation based on the detection optical path and reference optical path after coupling environmental interference. In each simulation, calculate the optical field distribution at the barrel detector position and the optical field distribution at the surface detector position respectively. Step 5: Construct a correlation function based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. Use the correlation function to inversely derive the light field distribution, and then calculate the ghost image of the object under test based on the second-order correlation function between the light field and the light intensity. Step 6: Perform histogram equalization on the ghost image of the object under test using Python to obtain the ghost image under the coupled fog and turbulence environment. The following relationship exists in the corresponding process: ； in, This represents the grayscale value of a pixel in the ghost image of the object under test. Indicates to Perform grayscale swapping. Indicates the grayscale depth. Indicates the number of pixels. Histogram distribution representing the ghost image of the object under test; In step 3, Gaussian blur is introduced into the probe optical path to simulate a foggy environment, and a random phase screen based on Perlin noise is introduced to simulate a turbulent environment. Specifically, this includes the following sub-steps: Gaussian blurring is applied to the light source propagating between the lens and the object under test to simulate the scattering and attenuation effects of fog on light propagation. The following relationship exists in the corresponding process: ； in, Indicates in Gaussian function value at that location, Indicates standard deviation, Represents an exponential function; Perlin noise modulation is applied to the light source propagating between the object under test and the barrel detector to simulate the random wavefront distortion effect caused by turbulent environment.

2. The ghost imaging monitoring method based on environmental coupling simulation and image enhancement according to claim 1, characterized in that, In step 1, the initial light source is split using a beam splitter matrix model to obtain the probe light source and the reference light source. The following relationship exists in the corresponding process: ； in, Indicates the light source of the detection optical path. Indicates the reference optical path source. Indicates the initial light source. Indicates the transmission coefficient. This represents the reflection coefficient.

3. The ghost imaging monitoring method based on environmental coupling simulation and image enhancement according to claim 2, characterized in that, In step 2, a lens, the object to be measured, and a barrel detector are sequentially arranged in the detection optical path, and a surface detector is arranged in the reference optical path. This includes the following sub-steps: In the detection optical path, the lens is defined as a phase modulation function; The object to be tested is defined as a two-dimensional binary mask. The element value of the two-dimensional binary mask is 0 or 1, where 0 represents opaqueness and 1 represents transparency. The function of the bucket detector is defined as integrating and summing the light intensity distribution arriving at the plane of the bucket detector. In the reference optical path, the function of the surface detector is defined as recording the two-dimensional intensity distribution of the light field arriving at the surface detector plane.

4. The ghost imaging monitoring method based on environmental coupling simulation and image enhancement according to claim 3, characterized in that, In the detection optical path, the lens is defined as a phase modulation function, and the following relationship exists in the corresponding process: ； in, Indicates the location Phase change at that point Represents the x-axis, Represents the ordinate, Indicates phase shift, Indicates wavelength. Indicates focal length; In the step of defining the object to be measured as a two-dimensional binary mask, where the element value of the two-dimensional binary mask is 0 or 1, where 0 represents opacity and 1 represents transparency, the following relationship exists: ； in, Represents a two-dimensional binary mask; In defining the function of the bucket detector as the step of integrating and summing the light intensity distribution arriving at the bucket detector plane, the following relationship exists: ； in, This represents the total light intensity value. This represents the light intensity passing through the object being measured.

5. The ghost imaging monitoring method based on environmental coupling simulation and image enhancement according to claim 4, characterized in that, In step 4, optical field propagation simulation is performed based on the probe optical path and reference optical path after coupling environmental interference. In each simulation, the optical field distribution at the bucket detector location and the optical field distribution at the surface detector location are calculated respectively. Specifically, the optical field propagation simulation is performed based on the probe optical path after coupling environmental interference, and the optical field distribution at the bucket detector location is calculated. The data collected by the bucket detector will have its confidence level calculated in the back end. The following relationship exists in the corresponding process: ； in, Indicates the bucket detector in three-dimensional coordinates light field distribution Represents three-dimensional coordinates. Represents the three-dimensional space occupied by the light source. Represents the light source in three-dimensional coordinates The complex amplitude distribution, The impulse response function from the light source to the lens is represented by the following. This represents the impulse response function from the lens to the object being measured. This represents the impulse response function of the object being measured to the barrel detector. This represents the transmission function of the object being measured.

6. The ghost imaging monitoring method based on environmental coupling simulation and image enhancement according to claim 5, characterized in that, In step 5, a correlation function is constructed based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. The distribution of the light field is then inverted using the correlation function. Finally, the ghost image of the object under test is calculated based on the second-order correlation function between the light field and the light intensity. The following relationship exists in the corresponding process: ； in, Represents the light field correlation function. This indicates the intensity distribution of the reference optical path. This indicates the light intensity distribution along the detection optical path. Indicates the complex amplitude of the optical field in the reference optical path. Indicates the complex amplitude of the optical field in the probe optical path. Indicates complex conjugation. This represents the average expectation.

7. A ghost imaging monitoring system based on environmental coupling simulation and image enhancement, characterized in that, The system employs the ghost imaging monitoring method based on environmental coupling simulation and image enhancement as described in any one of claims 1 to 6, and the system comprises: Ghost imaging module, used for: An initial light source is set, and the initial light source is split into beams using a beam splitter matrix model to obtain the probe light source and the reference light source. Based on the aforementioned detection optical path light source and reference optical path light source, a detection optical path and a reference optical path are constructed respectively; a lens, the object to be measured, and a barrel detector are sequentially arranged on the detection optical path, and a surface detector is arranged on the reference optical path; Gaussian blurring is introduced into the probe optical path to simulate a foggy environment, and a random phase screen based on Perlin noise is introduced to simulate a turbulent environment, so as to obtain the probe optical path after coupling environmental interference. The optical field propagation simulation is performed based on the detection optical path and the reference optical path after coupling environmental interference. In each simulation, the optical field distribution at the location of the barrel detector and the location of the surface detector are calculated respectively. A correlation function is constructed based on the light field distribution at the location of the barrel detector and the light field distribution at the location of the surface detector. The distribution of the light field is then inverted using the correlation function. Finally, the ghost image of the object under test is calculated based on the second-order correlation function between the light field and the light intensity. The histogram equalization module is used for: Based on Python, histogram equalization is performed on the ghost image of the object under test to obtain the ghost image under the coupled fog and turbulence environment.

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