Image noise analog noise adding method and device, equipment and medium

Abstract

The invention relates to an image noise analog noise adding method, device and equipment and a medium, and the method comprises the steps: obtaining a standard low-noise original collection data set, a standard high-noise original collection data set, and a to-be-processed low-noise collection data set; obtaining a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set; obtaining a pure response image set based on the to-be-processed low-noise acquisition data set, and performing noise addition on the pure response image set according to the target noise parameter set to obtain an analog noise addition image set; and carrying out correction processing on the to-be-processed low-noise acquisition data set and the analog noise-added image set. In the whole process, the standard low-noise original acquisition data set and the standard high-noise original acquisition data set are acquired, the target noise parameter set is systematically extracted, the statistical characteristics of the noise can be accurately described, and efficient and accurate low-noise image analog noise addition is realized through noise addition and correction processing.

Description

Technical Field

[0001] The present application relates to the technical field of image noise processing, and in particular to an image noise simulation and adding noise method, device, computer equipment, storage medium and computer program product. Background Art

[0002] X-ray array detectors play a vital role in medical imaging and industrial non-destructive testing, especially in high-resolution and low-dose imaging applications. With the increasingly stringent control of radiation dose in clinical practice, X-ray imaging technology under low-dose conditions faces significant challenges. Under low-dose conditions, X-ray signals are weak and noise problems are particularly prominent, which directly affects the quality of images and the accuracy of diagnosis.

[0003] Traditional noise determination and noise addition methods mainly rely on experimental measurements. Although these methods are intuitive, they have limitations such as high cost, long cycle, and difficulty in fully covering a variety of working conditions. These limitations seriously restrict the in-depth study and optimization design of the noise characteristics of X-ray array detectors.

[0004] Although existing simulation-based methods have made up for the shortcomings of experimental measurements to a certain extent, they still face great challenges in terms of modeling complexity and computational efficiency. Especially under low-dose conditions, the detector output signal is affected by multiple factors such as quantum noise and electronic noise. How to achieve efficient noise simulation and noise addition methods has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] Based on this, it is necessary to provide an efficient and accurate image noise simulation and noise adding method, device, computer equipment, computer readable storage medium and computer program product to address the above technical problems.

[0006] In a first aspect, the present application provides an image noise simulation and noise adding method. The method comprises:

[0007] Acquire a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a low-noise acquisition data set to be processed;

[0008] Obtaining a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0009] A pure response image set is obtained based on the low-noise acquisition data set to be processed, and noise is added to the pure response image set according to the target noise parameter set to obtain a simulated noisy image set;

[0010] Correction processing is performed on the low-noise acquisition data set to be processed and the simulated noisy image set to obtain a low-noise image set and a corresponding noisy image set.

[0011] In one embodiment, obtaining the target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set includes:

[0012] Obtaining the corresponding initial noise parameter set in the noise simulation formula according to the standard high-noise original acquisition data set;

[0013] Updating the initial noise parameter set according to the standard low-noise original acquisition data set to obtain the target noise parameter set.

[0014] In one embodiment, obtaining the corresponding initial noise parameter set in the noise simulation formula according to the standard high-noise original acquisition data set includes:

[0015] Generating a first pixel noise matrix according to the standard high-noise original acquisition data set;

[0016] Performing Gaussian fitting on each column of data in the first pixel noise matrix to obtain the mean value and the standard deviation;

[0017] Performing linear fitting on the arithmetic square root of the mean value and the standard deviation to obtain a fitted noise model, and extracting the corresponding initial noise parameter set in the noise simulation formula according to the fitted noise model.

[0018] In one embodiment, updating the initial noise parameter set according to the standard low-noise original acquisition data set to obtain the target noise parameter set includes:

[0019] Generating a second pixel noise matrix according to the standard low-noise original acquisition data set;

[0020] Updating the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain the target noise parameter set.

[0021] In one embodiment, updating the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain the target noise parameter set includes:

[0022] Calculating the high signal-to-noise ratio and the low signal-to-noise ratio respectively according to the first pixel noise matrix and the second pixel noise matrix;

[0023] Initializing the intensity ratio of Poisson noise with the ratio of the high signal-to-noise ratio and the low signal-to-noise ratio, and incorporating the intensity ratio as the initial intensity ratio into the initial noise parameter set to obtain a combined noise parameter set;

[0024] Substituting the combined noise parameter set into the noise simulation formula to add noise to the second pixel noise matrix;

[0025] Calculate the noise-added low signal-to-noise ratio of the second pixel noise matrix after adding noise;

[0026] Adjust the intensity ratio until the noise-added low signal-to-noise ratio is equal to the high signal-to-noise ratio, so as to update the noise parameter set and obtain the target noise parameter set.

[0027] In one embodiment, the generating the first pixel noise matrix according to the standard high-noise original acquisition data set includes:

[0028] Preprocess the standard high-noise original acquisition data set to obtain a pure response value data set;

[0029] For each batch of data in the pure response value data set, arrange the repeated acquisitions of a single pixel in a single column;

[0030] Arrange all pixel data columns into a two-dimensional matrix m in the column direction;

[0031] Arrange all the two-dimensional matrices m into a first pixel noise matrix in the column direction.

[0032] In one embodiment, the obtaining the standard low-noise original acquisition data set, the standard high-noise original acquisition data set, and the to-be-processed low-noise acquisition data set includes:

[0033] Collect the X-ray raw data when the standard part is statically placed in a low-noise environment to obtain the standard low-noise original acquisition data set;

[0034] Collect the X-ray raw data when the standard part is statically placed in a high-noise environment to obtain the standard high-noise original acquisition data set;

[0035] Collect the X-ray raw data of the object to be detected in a low-noise environment to obtain the to-be-processed low-noise acquisition data set.

[0036] In one embodiment, before the collecting the X-ray raw data when the standard part is statically placed in a low-noise environment to obtain the standard low-noise original acquisition data set, it further includes:

[0037] Determine the optical path environment and parameters of the X-ray area array detector;

[0038] Keep the optical path environment and parameters of the X-ray area array detector unchanged.

[0039] In a second aspect, the present application further provides an image noise simulation and noise-adding device. The device includes:

[0040] A data acquisition module, configured to acquire a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set;

[0041] A noise parameter calculation module, configured to obtain a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0042] A noise addition module, configured to obtain a pure response image set based on the to-be-processed low-noise acquisition data set, and perform noise addition on the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set;

[0043] A correction module, configured to perform correction processing on the to-be-processed low-noise acquisition data set and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

[0044] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0045] Obtain a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set;

[0046] Obtain a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0047] Obtain a pure response image set based on the to-be-processed low-noise acquisition data set, and perform noise addition on the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set;

[0048] Perform correction processing on the to-be-processed low-noise acquisition data set and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0050] Obtain a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set;

[0051] Obtain a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0052] Obtain a pure response image set based on the to-be-processed low-noise acquisition data set, and perform noise addition on the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set;

[0053] Perform correction processing on the to-be-processed low-noise acquisition data set and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

[0054] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program which, when executed by a processor, implements the following steps:

[0055] Obtain a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a low-noise acquisition data set to be processed;

[0056] Obtain a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0057] Obtain a pure response image set based on the low-noise acquisition data set to be processed, and add noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set;

[0058] Perform calibration processing on the low-noise acquisition data set to be processed and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

[0059] For the above image noise simulation and noise addition method, device, computer device, storage medium, and computer program product, a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a low-noise acquisition data set to be processed are obtained; a target noise parameter set is obtained according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set; a pure response image set is obtained based on the low-noise acquisition data set to be processed, and the pure response image set is added with noise according to the target noise parameter set to obtain a simulated noise-added image set; calibration processing is performed on the low-noise acquisition data set to be processed and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set. Throughout the process, by obtaining the standard low-noise original acquisition data set and the standard high-noise original acquisition data set, the target noise parameter set is systematically extracted, which can accurately describe the statistical characteristics of the noise, and through noise addition and calibration processing, the generated noise-added image set can highly restore the actual noise distribution characteristics, realizing efficient and accurate low-noise image simulation and noise addition. Description of the Drawings

[0060] Figure 1 It is an application environment diagram of the image noise simulation and noise addition method in an embodiment;

[0061] Figure 2 It is a flowchart of the image noise simulation and noise addition method in an embodiment;

[0062] Figure 3 It is a flowchart of the image noise simulation and noise addition method in another embodiment;

[0063] Figure 4 It is a sub-flowchart of S400 in an embodiment;

[0064] Figure 5 Schematic diagram of the fitting result of noise parameters;

[0065] Figure 6 Schematic flow chart of the image noise simulation and adding noise method in one specific application example;

[0066] Figure 7 Block diagram of the structure of the image noise simulation and adding noise device in one embodiment;

[0067] Figure 8 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0068] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further details the present application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] To elaborate on the application scenario, core technical principle and finally achieved technical effect of the image noise simulation and adding noise method of the present application, the following will first explain the entire technical principle based on the X-ray image noise theory.

[0070] Low-dose X-ray imaging is an important technical means. However, under low-dose conditions, due to the weak X-ray signal, the noise problem in the image is particularly prominent, directly affecting the imaging quality and diagnostic accuracy. The noise in low-dose X-ray images mainly includes quantum noise that follows the Poisson distribution and electronic noise that follows the Gaussian distribution, with quantum noise being the main one. Quantum noise comes from X-ray photons, while Gaussian noise mainly comes from the electronic noise of the acquisition circuit. The noise generation process can be clearly expressed by the following formula:

[0071]

[0072] Where D is the signal output value of the acquisition circuit, μD is the average value of the signal output (used to approximate the noise-free signal value), α is the scaling factor of Poisson noise, μD / α is the intensity of the signal before entering the acquisition circuit, and Gaussian(0,σ) is a Gaussian distribution with a mean of 0 and a standard deviation of σ.

[0073] In the present invention, we assume that the normal image acquisition environment is a high-noise environment, and suppress the noise by increasing the ray source current and the detector integration time to form an environment with lower noise, which we assume to be a low-noise environment. Therefore, these two environments only differ in the increase of the ray source current and the detector integration time, and the others remain the same. Further expansion based on the above theory is as follows:

[0074] Detailed derivation and explanation of the noise simulation formula

[0075] Definition: X-ray photon count N, μ N is the mean value of the X-ray photon count (approximate noise-free photon count value); σ D is the standard deviation of the signal output. μ D and σ D can be obtained from the observed data D:

[0076] μ D = α * μ N (assuming this linear relationship reasonably), N ~ Poisson(μ N );

[0077]

[0078] Let σ D = y, σ = b;

[0079] Poisson(μ N ) is a Poisson distribution with parameter μ N . Through the observed data D, linear fitting = k * x + b, k and b can be obtained; that is, the noise simulation parameters α and σ are obtained; the noise simulation formula is as follows:

[0080] where μ D is the mean value of the repeatedly observed data D.

[0081] The difference in the noise parameters between the low-noise environment and the high-noise environment lies only in the different α values. Let α addnoise = factor * α lownoise Adjust factor, and set "D-lownoise" as the low-noise data. Substitute it into the noise simulation formula:

[0082]

[0083] When factor makes snr addnoise = snr noise , the final parameters are obtained The final simulated noise addition formula is:

[0084]

[0085] where: α addnoise is the α during simulated noise addition, α lownoise is the α in the low-noise situation, μ D-lownoise is the μ in the low-noise situation D , factor is the Poisson noise adjustment factor between the low-noise situation and the normal noise situation, snr addnoisevs. SNR noise respectively represent the data signal-to-noise ratios in the simulated noise-added and normal noise scenarios, and D addnoise is the data after simulated noise addition. The above derivations and explanations have detailedly explained how to calculate the noise parameters in a low-noise environment from the observed data D, and then by adjusting the factor, obtain the noise addition formula for simulating noise addition from a low-noise image to a high-noise environment.

[0086] Based on the above technical principle, the image noise simulation and addition method provided by the embodiments of this application can be applied to, for example, Figure 1 the application environment shown in the figure. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 sends an image noise simulation and addition request to the server 104, and the server 104 responds to the image noise simulation and addition request, and obtains a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set; according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set, obtains a target noise parameter set; based on the to-be-processed low-noise acquisition data set, obtains a pure response image set, and adds noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set; performs calibration processing on the to-be-processed low-noise acquisition data set and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set. Further, the server 104 can feedback the generated low-noise image set and the corresponding noise-added image set to the terminal 102. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0087] In one embodiment, as Figure 2 shown, a method for simulating and adding noise to an image is provided. Taking the method applied to the Figure 1 server 104 in the figure as an example, the method includes the following steps:

[0088] S200: Obtain a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set.

[0089] Standard low-noise original acquisition dataset: This is a set of image data with a relatively low noise level, serving as a benchmark for subsequent comparison and calibration. These data are typically obtained from high-quality image acquisition devices under relatively ideal low-noise acquisition environments. Standard high-noise original acquisition dataset: Corresponding to the standard low-noise dataset, this is a set of image data with a relatively high noise level. This set of data is used to extract and analyze the target noise characteristics and is the basis for generating the target noise parameter set. Low-noise acquisition dataset to be processed: This is the original image data that needs to be added with noise processing. It is usually also low-noise, but it is hoped to evaluate or enhance its noise robustness by simulating the noise addition process. Specifically, here, the above datasets that have been acquired by external acquisition devices can be directly obtained, or the above datasets can be directly acquired in a specific environment. Taking the acquisition of the standard low-noise original acquisition dataset as an example, data acquisition conditions: In a low-noise environment (such as high dose or long integration time), use an X-ray area array detector to repeatedly acquire a static standard part (such as a uniform phantom) multiple times. Parameter settings: The X-ray source voltage (e.g., 80 kV), current (e.g., 5 mA), detector integration time (e.g., 2 ms), and gain (e.g., medium range) are stable.

[0090] S400: Based on the standard high-noise original acquisition dataset and the standard low-noise original acquisition dataset, obtain the target noise parameter set.

[0091] Next, use the standard high-noise original acquisition dataset and the standard low-noise original acquisition dataset for comparative analysis to extract the target noise parameter set. Specifically, it can be processed through methods such as noise matrix, Gaussian fitting, and linear modeling to obtain the target noise parameter set.

[0092] S600: Based on the low-noise acquisition dataset to be processed, obtain a pure response image set, and add noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set.

[0093] Based on the low-noise acquisition dataset to be processed, first obtain a pure response image set through preprocessing steps (such as removing the background, enhancing contrast, etc.). The pure response image set focuses more on the image information of the target object and reduces the interference of irrelevant backgrounds. Subsequently, according to the target noise parameter set obtained in S400, add noise to each image in the pure response image set. The noise addition process can be implemented through software algorithms, such as adding Gaussian noise, Poisson noise, etc., and the specific form depends on the content of the target noise parameter set. Further, multiple noise additions can be performed. The purpose of multiple noise additions is to ensure the randomness of the simulated noise to better reflect the authenticity of the noise in the actual environment.

[0094] S800: Perform calibration processing on the low-noise acquisition dataset to be processed and the simulated noise-added image set to obtain a low-noise image set and the corresponding noise-added image set.

[0095] Finally, perform calibration processing on the original low-noise acquisition data set to be processed and the image set after simulated noise addition. The purpose of calibration is to adjust parameters such as brightness and contrast between the two to make them visually consistent or achieve the best match under specific measurement criteria, thereby ensuring the effectiveness and accuracy of subsequent analysis or testing. After calibration processing, a low-noise image set (i.e., a version of the original low-noise acquisition data set to be processed after possible fine-tuning) and a corresponding noise-added image set (i.e., the image set after simulated noise addition, corresponding to the low-noise image set in content but containing the simulated target noise) are obtained.

[0096] The above image noise simulation and addition method obtains a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a low-noise acquisition data set to be processed; obtains a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set; obtains a pure response image set based on the low-noise acquisition data set to be processed, and adds noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set; performs calibration processing on the low-noise acquisition data set to be processed and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set. Throughout the process, by obtaining the standard low-noise original acquisition data set and the standard high-noise original acquisition data set, systematically extracting the target noise parameter set can accurately describe the statistical characteristics of the noise, and through noise addition and calibration processing, the generated noise-added image set can highly restore the actual noise distribution characteristics, realizing efficient and accurate low-noise image simulation and addition; overcoming the defects in traditional noise measurement and addition methods such as high cost, long cycle, difficulty in comprehensively covering various working conditions, and limitations; as well as problems such as high modeling complexity and low computational efficiency in traditional noise simulation and addition methods.

[0097] Furthermore, in practical applications, the image noise simulation and addition method of this application can be optionally adopted and applied to simulate noise scenarios to efficiently and accurately generate noise under various working conditions and environments, significantly reducing the cost of collecting a large amount of noise data. Taking the construction and training of a neural network model in medical imaging as an example, the image noise simulation and addition method of this application can be optionally adopted to preprocess the noise data, simulate the noise in different scenarios, and enrich the training data set to improve the accuracy and efficiency of subsequent neural network training.

[0098] In one embodiment, as Figure 3 shown, S400 includes:

[0099] S420: Obtain the corresponding initial noise parameter set in the noise simulation formula according to the standard high-noise original acquisition data set.

[0100] First, conduct an in-depth analysis of the standard high-noise original acquisition dataset. This dataset contains rich noise information and is a key source for extracting noise characteristics. Through various technical means such as mathematical statistics, frequency-domain analysis, and texture recognition, various features of the noise are extracted and quantified from the standard high-noise dataset, such as noise intensity, distribution type, frequency components, etc. Based on the above analysis, construct a noise simulation formula that can generate noise similar to the standard high-noise dataset based on a series of initial noise parameters (such as noise mean, variance, spatial correlation parameters, etc.). Extract these initial noise parameters from the formula to form an initial noise parameter set. This step provides a basic framework for subsequent noise simulation.

[0101] S440: Update the initial noise parameter set according to the standard low-noise original acquisition dataset to obtain the target noise parameter set.

[0102] Use the standard low-noise original acquisition dataset to verify and adjust the initial noise parameter set. Compare and analyze the standard low-noise dataset and the noise image generated by simulating with the initial noise parameter set, and identify the differences between the two. According to these differences, fine-tune the initial noise parameter set to reduce the deviation between the simulated noise and the real low-noise environment. After multiple iterative adjustments, until the simulated noise image and the standard low-noise dataset reach a satisfactory matching degree visually or under specific metric criteria. At this time, the updated noise parameter set is the target noise parameter set, which more accurately reflects the characteristics of the target noise and provides a reliable basis for subsequent noise addition processing.

[0103] In one of the embodiments, as Figure 3 , Figure 4 shown, S420 includes:

[0104] S422: Generate the first pixel noise matrix according to the standard high-noise original acquisition dataset.

[0105] Extract the image data from the standard high-noise original acquisition dataset and convert it into a two-dimensional matrix form for subsequent analysis. Here, we call it the first pixel noise matrix. Each column of the first pixel noise matrix can represent the data set obtained by repeatedly collecting data of a certain pixel in a batch.

[0106] S424: Perform Gaussian fitting on the data of each column in the first pixel noise matrix to obtain the mean and standard deviation.

[0107] Perform Gaussian fitting on each column of data (or row data selected according to analysis needs) in the first pixel noise matrix. Gaussian fitting is a probability density fitting method used to determine whether the data conforms to a Gaussian distribution and estimate its mean (μ) and standard deviation (σ). In this step, it is assumed that the noise data approximately conforms to a Gaussian distribution, so the mean and standard deviation of each column of data are extracted through Gaussian fitting. These means and standard deviations reflect the intensity and dispersion of noise at different positions or features. Specifically, since the Poisson distribution can be fully approximated by the Gaussian distribution, we directly fit the data D with the Gaussian distribution to obtain the mean (μ) and the standard deviation (σ) after the superposition of two types of noise. Figure 5 Is an example of fitting noise parameters.

[0108] S426: Perform a linear fit on the square root of the mean and the standard deviation to obtain the fitted noise model, and extract the corresponding initial noise parameter set in the noise simulation formula according to the fitted noise model.

[0109] Take the square root of the mean and the standard deviation of all columns obtained in S424 as data points and perform a linear fit. The linear fit aims to determine whether there is a linear relationship between the square root of the mean and the standard deviation, which helps to understand the spatial distribution characteristics of the noise. Based on the results of the linear fit, construct a noise model. This model can be a simple linear equation used to describe the law of the square root of the noise mean and the standard deviation changing with the image position or features. The noise model not only provides an in-depth understanding of the noise characteristics but also provides a mathematical basis for subsequent noise simulation. According to the constructed noise model, extract the corresponding initial noise parameter set in the noise simulation formula. These parameters may include the slope and intercept of the linear fit equation, as well as any additional parameters related to the noise distribution (such as the shape parameters of the Gaussian distribution, etc.). The initial noise parameter set is the core of the subsequent simulated noise addition process, and they determine the basic characteristics and distribution law of the simulated noise.

[0110] In one embodiment, as Figure 3 、 Figure 4 shown, S440 includes:

[0111] S442: Generate a second pixel noise matrix according to the standard low-noise original acquisition data set.

[0112] Similarly, extract the image data from the standard low-noise original acquisition data set and convert it into the corresponding second pixel noise matrix. Here, the first pixel noise matrix and the second pixel noise matrix are consistent in dimension and structure, facilitating subsequent comparison and analysis.

[0113] S444: Update the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain the target noise parameter set.

[0114] Update the initial noise parameter set using the second pixel noise matrix. The purpose of this step is to ensure that the simulated noise not only conforms to the high-noise data set but also reflects the characteristics of the low-noise data set to a certain extent, thereby generating a more realistic and accurate simulated noisy image. The specific update method may include: comparing the differences in statistical characteristics (such as mean, variance), frequency-domain characteristics, or texture characteristics between the first pixel noise matrix and the second pixel noise matrix, and adjusting the relevant parameters in the initial noise parameter set according to these differences. The adjustment process requires multiple iterations until the simulated noisy image achieves a satisfactory match with the low-noise data set visually or under specific metrics. After iterative adjustment, the updated noise parameter set obtained is the target noise parameter set.

[0115] In one embodiment, updating the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain the target noise parameter set includes:

[0116] Step 1: Calculate the high signal-to-noise ratio and the low signal-to-noise ratio according to the first pixel noise matrix and the second pixel noise matrix respectively.

[0117] Calculate the high signal-to-noise ratio (SNR_high) and the low signal-to-noise ratio (SNR_low) according to the first pixel noise matrix and the second pixel noise matrix respectively. The signal-to-noise ratio is usually defined as the ratio of signal power to noise power, but in image processing, it can be estimated through the statistical characteristics of image pixel values.

[0118] Step 2: Initialize the intensity ratio of Poisson noise with the ratio of the high signal-to-noise ratio and the low signal-to-noise ratio, and incorporate the intensity ratio into the initial noise parameter set to obtain the combined noise parameter set.

[0119] In many imaging systems, Poisson noise is a common type of noise, and its intensity is proportional to the signal intensity. Therefore, the intensity ratio of Poisson noise can be estimated by comparing the difference between the high signal-to-noise ratio and the low signal-to-noise ratio. Specifically, the ratio of the signal-to-noise ratio of the first pixel noise matrix (high signal-to-noise ratio) / the signal-to-noise ratio of the second pixel noise matrix (low signal-to-noise ratio) can be used as the initial intensity ratio (factor) of Poisson noise, which is used to reflect the relative growth of Poisson noise during the process from low noise to high noise.

[0120] Step 3: Substitute the combined noise parameter set into the noise simulation formula to add noise to the second pixel noise matrix.

[0121] Incorporate the calculated intensity ratio of Poisson noise into the initial noise parameter set to obtain the combined noise parameter set. The initial noise parameter set may include parameters such as the mean and variance of Gaussian noise, and the addition of the intensity ratio of Poisson noise makes the noise model more perfect.

[0122] Step 4: Calculate the added low signal-to-noise ratio of the second pixel noise matrix after adding noise.

[0123] Using the combined noise parameter set and the noise simulation formula, add noise to the second pixel noise matrix. The noise simulation formula can be understood as a hybrid model based on Poisson distribution and Gaussian distribution, which can generate simulated noise similar to the real noise. Calculate the added low signal-to-noise ratio (SNR_added) for the second pixel noise matrix after adding noise. This step is to verify whether the added noise effect meets the expectations.

[0124] Step 5: Adjust the intensity ratio until the added low signal-to-noise ratio is equal to the high signal-to-noise ratio to update the noise parameter set and obtain the target noise parameter set.

[0125] Compare the difference between the added low signal-to-noise ratio and the high signal-to-noise ratio. If they are not equal, adjust the Poisson noise intensity ratio (factor) and perform noise simulation and noise addition again. This adjustment process requires multiple iterations until the added low signal-to-noise ratio is equal to the high signal-to-noise ratio or within a predetermined error range. After iterative adjustment, the updated noise parameter set obtained is the target noise parameter set. Further, a greedy search can be performed within a certain value range after discretization to find a satisfactory solution faster, thus replacing the iterative adjustment process.

[0126] In one of the embodiments, generating the first pixel noise matrix according to the standard high-noise original acquisition data set includes:

[0127] Step 1: Preprocess the standard high-noise original acquisition data set to obtain a pure response value data set.

[0128] Preprocess the standard high-noise original acquisition data set to remove or reduce the influence of non-noise factors (such as uneven device response, light change, etc.) on the image. The preprocessing steps may include image enhancement, denoising (initially, to retain necessary noise features), calibration, etc. After preprocessing, a pure response value data set is obtained. The pure response value data set refers to the image data that only contains signals and noise after removing most non-noise factors.

[0129] Step 2: For each batch of data in the pure response value data set, arrange the repeated acquisitions of a single pixel in a single column.

[0130] For each batch of data in the pure response value data set, arrange the repeated acquisition values of a single pixel in a column. This is to process the multiple measurement values of each pixel as time series data, so as to more accurately evaluate the noise characteristics. Repeated acquisition refers to multiple measurements of the same pixel at different time points under the same conditions.

[0131] Step 3: Stack all the pixel data columns into a two-dimensional matrix m in the column direction.

[0132] Concatenate all pixel metadata columns into a two-dimensional matrix m in the column direction. Each two-dimensional matrix m represents the image data under a specific batch or condition, where each column is the value of multiple repeated measurements of a pixel.

[0133] Step 4: Concatenate all two-dimensional matrices m in the column direction to form the first pixel noise matrix.

[0134] Concatenate all two-dimensional matrices m again in the column direction to form a larger two-dimensional matrix, which is the first pixel noise matrix. This first pixel noise matrix contains the image data under all batches or conditions, and the multiple measurement values of each pixel are integrated together. Each column of the first pixel noise matrix may represent multiple measurements of a pixel at different time points under the same condition (or noise level).

[0135] The process of generating the second pixel noise matrix is similar to the above process of generating the first pixel noise matrix, and will not be elaborated here.

[0136] In one embodiment, obtaining the standard low-noise original acquisition dataset, the standard high-noise original acquisition dataset, and the low-noise acquisition dataset to be processed includes:

[0137] Step 1: Acquire the X-ray raw data when the standard part is statically placed in a low-noise environment to obtain the standard low-noise original acquisition dataset.

[0138] Select a standard test piece (such as a metal block, calibration plate, etc.), ensure that its surface is smooth, defect-free, and the material composition is uniform. Place the standard part statically in the detection area of the X-ray imaging device, and ensure that it remains stationary throughout the acquisition process. Adjust the parameters of the X-ray imaging device, such as tube current, exposure time, etc., to minimize noise generation. This usually means choosing a higher tube current and a longer exposure time, but it should be done on the premise of ensuring image quality. Ensure that the imaging environment is stable and avoid external interference (such as vibration, temperature change, etc.). Start the X-ray imaging device and perform X-ray imaging on the standard part. The acquired X-ray raw data is the standard low-noise original acquisition dataset. These data may include multiple image frames or scan lines for subsequent analysis and processing.

[0139] Step 2: Acquire the X-ray raw data when the standard part is statically placed in a high-noise environment to obtain the standard high-noise original acquisition dataset.

[0140] Similar to the above Step 1, select the same standard parts and statically place them in the detection area of the X-ray imaging device. Adjust the parameters of the X-ray imaging device to simulate a high-noise environment. This may mean reducing the tube current, shortening the exposure time, etc., to promote the generation of noise. Start the X-ray imaging device and perform X-ray imaging on the standard parts. The acquired raw X-ray data is the standard high-noise raw acquisition dataset.

[0141] Step 3: Acquire the raw X-ray data of the object to be detected in a low-noise environment to obtain the low-noise acquisition dataset to be processed.

[0142] Select the object to be detected (such as industrial components, biological samples, etc.), and ensure that it is placed in the detection area of the X-ray imaging device. Depending on the characteristics of the object to be detected and the imaging requirements, it may be necessary to preprocess the object to be detected (such as cleaning, fixing, etc.). Adjust the parameters of the X-ray imaging device to minimize the generation of noise. Ensure that the imaging environment is stable and avoid external interference. Start the X-ray imaging device and perform multiple X-ray imaging on the object to be detected to obtain multiple image frames or scan lines. The set of acquired raw X-ray data is the low-noise acquisition dataset to be processed. These data will be used for subsequent noise simulation and noise addition processing and image quality evaluation.

[0143] In one embodiment, before acquiring the raw X-ray data of the standard parts statically placed in a low-noise environment to obtain the standard low-noise raw acquisition dataset, it further includes:

[0144] Determine the optical path environment and parameters of the X-ray area array detector; keep the optical path environment and parameters of the X-ray area array detector unchanged.

[0145] In this embodiment, before acquiring the raw X-ray data of the standard parts statically placed in a low-noise environment to obtain the standard low-noise raw acquisition dataset, pay attention to and determine the optical path environment and parameters of the X-ray area array detector, and ensure the stability of these environments and parameters. Specifically, determining the optical path environment and parameters includes the ray source voltage and current, the detector integration time and gain. If dynamic image acquisition is required, it also includes the detection speed. Keeping the voltage and detector gain unchanged specifically includes ensuring that the voltage and detector gain remain constant throughout the acquisition process.

[0146] To illustrate in detail the technical solution and its technical principle of the image noise simulation and noise addition method of the present application, the following will be described with specific application examples. As Figure 6 shown, in one specific application example, the image noise simulation and noise addition method of the present application includes the following steps:

[0147] 1. Preparation stage

[0148] 1.1 Determine the optical path environment and parameters; including the ray source voltage and current, the detector integration time and gain. If dynamic image acquisition is required, the detection speed is also included.

[0149] 1.2 Keep the voltage and detector gain unchanged; ensure that the voltage and detector gain remain constant throughout the acquisition process.

[0150] 2. Data acquisition stage

[0151] 2.1 Data acquisition in a low-noise environment; 2.1.1 Acquire the original X-ray data when the standard part is statically placed; record a single placement as one batch of acquisition, and each batch of acquisition needs to be repeated several times. According to the statistical law, the more times of repetition, the more accurately the data can reflect the noise statistical parameters. 2.1.2 Save the data; all acquired data is saved as data_lownoise_static. At the same time, in this scenario, the data acquired during normal detection is saved as data_lownoise_normal.

[0152] 2.2 Data acquisition in a high-noise environment; 2.2.1 Acquire the original X-ray data (including the object to be detected) when the standard part is statically placed; record a single placement as one batch of acquisition, and each batch of acquisition can be repeated several times. 2.2.2 Save the data; all acquired data is saved as data_noise_static.

[0153] 3. Data induction and sorting stage

[0154] 3.1 Extract and preprocess the data; 3.1.1 Extract data_noise_static and preprocess it to obtain a pure response value data set. 3.1.2 Extract data_lownoise_static and perform corresponding processing.

[0155] 3.2 Data arrangement and matrix formation For each batch of data, arrange the repeated acquisitions of a single pixel element in a single column, and then splice all the pixel element data columns into two-dimensional matrices M_lownoise and M_noise in the column direction.

[0156] 4. Signal-to-noise ratio calculation and noise simulation stage

[0157] 4.1 Calculate the signal-to-noise ratio Calculate the signal-to-noise ratio based on M_lownoise and M_noise, which are snr_lownoise and snr_noise respectively.

[0158] 4.2 Gaussian fitting and linear fitting. Perform Gaussian fitting on each column of data in M_noise to obtain the mean value (x 2 ) and the standard deviation (y). Perform linear fitting on x and y to obtain the result fittedmodel_noise.

[0159] 4.3 Application of Noise Simulation Formula Denote the intensity ratio value of Poisson noise between high noise and low noise as factor, initialize factor = snr_noise / snr_lownoise, and incorporate factor into alpha. Substitute alpha into the noise simulation formula to add noise to M_lownoise and then calculate its signal-to-noise ratio snr_addnoise. Adjust factor such that snr_addnoise = snr_noise, and update the noise parameter set alpha.

[0160] 5. Image Correction and Simulated Noise Addition Stage

[0161] 5.1 Image Preprocessing and Correction Extract data_lownoise_normal for preprocessing to obtain the pure response value image set image_lownoise_normal.

[0162] 5.2 Simulated Noise Addition Substitute alpha into the noise simulation formula to add noise to each image in image_lownoise_normal to obtain simulated noise-added images. Repeat the above steps n times to obtain the simulated noise-added image set image_pure_addnoise.

[0163] 5.3 Correction to Obtain the Final Image Correct image_lownoise_normal and image_pure_addnoise to obtain the final low-noise image set image_lownoise and the corresponding noise-added image image_addnoise.

[0164] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0165] Based on the same inventive concept, an embodiment of the present application further provides an image noise simulation and noise addition device for implementing the above-mentioned image noise simulation and noise addition method. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the image noise simulation and noise addition device provided below can refer to the limitations on the image noise simulation and noise addition method in the above text, and will not be repeated here.

[0166] In one embodiment, as Figure 7 shown, an image noise simulation and noise addition device is provided, including:

[0167] A data acquisition module 200, configured to acquire a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a to-be-processed low-noise acquisition data set;

[0168] A noise parameter calculation module 400, configured to obtain a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set;

[0169] A noise addition module 600, configured to obtain a pure response image set based on the to-be-processed low-noise acquisition data set, and add noise to the pure response image set according to the target noise parameter set to obtain a simulated noise addition image set;

[0170] A correction module 800, configured to perform correction processing on the to-be-processed low-noise acquisition data set and the simulated noise addition image set to obtain a low-noise image set and a corresponding noise addition image set.

[0171] In one of the embodiments, the noise parameter calculation module 400 is further configured to obtain an initial noise parameter set corresponding in the noise simulation formula according to the standard high-noise original acquisition data set; update the initial noise parameter set according to the standard low-noise original acquisition data set to obtain the target noise parameter set.

[0172] In one of the embodiments, the noise parameter calculation module 400 is further configured to generate a first pixel noise matrix according to the standard high-noise original acquisition data set; perform Gaussian fitting on each column of data in the first pixel noise matrix to obtain a mean value and a standard deviation; perform linear fitting on the arithmetic square root of the mean value and the standard deviation to obtain a fitted noise model, and extract the initial noise parameter set corresponding in the noise simulation formula according to the fitted noise model.

[0173] In one of the embodiments, the noise parameter calculation module 400 is further configured to generate a second pixel noise matrix according to the standard low-noise original acquisition data set; update the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain the target noise parameter set.

[0174] In one embodiment, the noise parameter calculation module 400 is further configured to calculate a high signal-to-noise ratio and a low signal-to-noise ratio according to the first pixel noise matrix and the second pixel noise matrix respectively; initialize the intensity ratio of Poisson noise with the ratio of the high signal-to-noise ratio to the low signal-to-noise ratio, and incorporate the intensity ratio as the initial intensity ratio into the initial noise parameter set to obtain a merged noise parameter set; substitute the merged noise parameter set into the noise simulation formula to add noise to the second pixel noise matrix; calculate the added-noise low signal-to-noise ratio of the second pixel noise matrix after adding noise; adjust the intensity ratio until the added-noise low signal-to-noise ratio is equal to the high signal-to-noise ratio to update the noise parameter set and obtain a target noise parameter set.

[0175] In one embodiment, the noise parameter calculation module 400 is further configured to preprocess the standard high-noise original acquisition data set to obtain a pure response value data set; for each batch of data in the pure response value data set, arrange the repeated acquisitions of a single pixel in a single column; splice all the pixel data columns into a two-dimensional matrix m in the column direction; and splice all the two-dimensional matrices m into a first pixel noise matrix in the column direction.

[0176] In one embodiment, the data acquisition module 200 is further configured to acquire X-ray raw data when a standard part is statically placed in a low-noise environment to obtain a standard low-noise original acquisition data set; acquire X-ray raw data when the standard part is statically placed in a high-noise environment to obtain a standard high-noise original acquisition data set; and acquire X-ray raw data of the object to be detected in a low-noise environment to obtain a to-be-processed low-noise acquisition data set.

[0177] In one embodiment, the data acquisition module 200 is further configured to determine the optical path environment and parameters of the X-ray area array detector; and maintain the optical path environment and parameters of the X-ray area array detector unchanged.

[0178] Each module in the above image noise simulation and noise addition device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0179] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 8As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store preset data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements an image noise simulation and noise addition method.

[0180] Those skilled in the art can understand that Figure 8 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0181] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the above-mentioned image noise simulation and noise addition method is implemented.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the above-mentioned image noise simulation and noise addition method is implemented.

[0183] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the above-mentioned image noise simulation and noise addition method is implemented.

[0184] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-described method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0185] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0186] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An image noise simulation and noise addition method, characterized in that, The method includes: Obtaining a standard low-noise original acquisition dataset, a standard high-noise original acquisition dataset, and a low-noise acquisition dataset to be processed; Obtaining a target noise parameter set according to the standard high-noise original acquisition dataset and the standard low-noise original acquisition dataset; Obtaining a pure response image set based on the low-noise acquisition dataset to be processed, and adding noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set; Performing calibration processing on the low-noise acquisition dataset to be processed and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

2. The method according to claim 1, wherein The obtaining a target noise parameter set according to the standard high-noise original acquisition dataset and the standard low-noise original acquisition dataset includes: Obtaining a corresponding initial noise parameter set in the noise simulation formula according to the standard high-noise original acquisition dataset; Updating the initial noise parameter set according to the standard low-noise original acquisition dataset to obtain a target noise parameter set.

3. The method according to claim 2, wherein The obtaining a corresponding initial noise parameter set in the noise simulation formula according to the standard high-noise original acquisition dataset includes: Generating a first pixel noise matrix according to the standard high-noise original acquisition dataset; Performing Gaussian fitting on each column of data in the first pixel noise matrix to obtain a mean value and a standard deviation; Performing linear fitting on the arithmetic square root of the mean value and the standard deviation to obtain a fitted noise model, and extracting a corresponding initial noise parameter set in the noise simulation formula according to the fitted noise model.

4. The method according to claim 3, wherein The updating the initial noise parameter set according to the standard low-noise original acquisition dataset to obtain a target noise parameter set includes: Generating a second pixel noise matrix according to the standard low-noise original acquisition dataset; Updating the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain a target noise parameter set.

5. The method according to claim 4, wherein The updating the initial noise parameter set according to the first pixel noise matrix and the second pixel noise matrix to obtain a target noise parameter set includes: Calculating a high signal-to-noise ratio and a low signal-to-noise ratio according to the first pixel noise matrix and the second pixel noise matrix respectively; Initializing an intensity ratio of Poisson noise with the ratio of the high signal-to-noise ratio to the low signal-to-noise ratio, and incorporating the intensity ratio as an initial intensity ratio into the initial noise parameter set to obtain a combined noise parameter set; Substituting the combined noise parameter set into the noise simulation formula to add noise to the second pixel noise matrix; Calculating the signal-to-noise ratio after adding noise of the second pixel noise matrix after adding noise; Adjusting the intensity ratio until the signal-to-noise ratio after adding noise is equal to the high signal-to-noise ratio to update the noise parameter set to obtain a target noise parameter set.

6. The method according to claim 3, wherein The generating a first pixel noise matrix according to the standard high-noise original acquisition dataset includes: Performing preprocessing on the standard high-noise original acquisition dataset to obtain a pure response value dataset; For each batch of data in the pure response value dataset, arranging the repeated acquisitions of a single pixel in a single column; Arranging all pixel data columns into a two-dimensional matrix m in the column direction; Arranging all the two-dimensional matrices m into a first pixel noise matrix in the column direction.

7. The method according to claim 1, wherein The obtaining of the standard low-noise original acquisition data set, the standard high-noise original acquisition data set, and the low-noise acquisition data set to be processed includes: Collecting X-ray original data when a standard part is statically placed in a low-noise environment to obtain a standard low-noise original acquisition data set; Collecting X-ray original data when a standard part is statically placed in a high-noise environment to obtain a standard high-noise original acquisition data set; Repeatedly collecting X-ray original data of the object to be detected in a low-noise environment to obtain a low-noise acquisition data set to be processed.

8. The method according to claim 7, wherein Before collecting the X-ray original data when a standard part is statically placed in a low-noise environment to obtain a standard low-noise original acquisition data set, it further includes: Determining the optical path environment and parameters of the X-ray area array detector; Maintaining the optical path environment and parameters of the X-ray area array detector unchanged.

9. An image noise simulation and noise addition device, characterized in that, The device includes: A data acquisition module for obtaining a standard low-noise original acquisition data set, a standard high-noise original acquisition data set, and a low-noise acquisition data set to be processed; A noise parameter calculation module for obtaining a target noise parameter set according to the standard high-noise original acquisition data set and the standard low-noise original acquisition data set; A noise addition module for obtaining a pure response image set based on the low-noise acquisition data set to be processed and adding noise to the pure response image set according to the target noise parameter set to obtain a simulated noise-added image set; A correction module for performing correction processing on the low-noise acquisition data set to be processed and the simulated noise-added image set to obtain a low-noise image set and a corresponding noise-added image set.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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