Image noise reduction method and system, computer equipment and storage medium

Abstract

The invention provides an image noise reduction method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: firstly converting an original strip mine coal rock image into a gray level image, and carrying out the image processing of the gray level image for the problem of edge blurring caused by mixed noise in the strip mine coal rock image; the method comprises the following steps: firstly, extracting a noise model, analyzing the noise type and distribution characteristics of the noise model to implement preliminary noise reduction, filtering out small-scale noise of an image subjected to preliminary noise reduction through Gaussian-Laplacian joint transformation to obtain an enhanced image, then calculating a gradient magnitude image of the enhanced image, dynamically generating a parameter matrix in combination with a normalized gradient value, and constructing an adaptive generalized overall variation noise reduction model; and taking the gradient amplitude image and the self-adaptive parameter matrix as input, updating the optimal estimation image through iterative optimization, and obtaining an optimal noise reduction image when the iteration energy variation is smaller than a threshold value. According to the method, while mixed noise is removed, coal rock texture details are remarkably reserved, and high-quality data support is provided for mining decisions of strip mines.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, and specifically relates to an image noise reduction method, system, computer device, and storage medium. Background Technology

[0002] With the development of satellite remote sensing technology, Google remote sensing satellite imagery provides a wealth of data for open-pit mine monitoring and other related work. However, open-pit coal and rock images often contain significant noise due to the complex acquisition environment. This noise severely hinders subsequent critical tasks such as accurate extraction of coal and rock features and identification of ore layer distribution. For example, atmospheric scattering, sensor thermal noise, and interference from reflections from complex surface topography result in images containing various mixed noises, including salt-and-pepper noise and Gaussian noise.

[0003] Traditional noise reduction methods struggle to accommodate different noise types and are insufficient in preserving the detailed features of coal and rock images. There is an urgent need for an efficient noise reduction method specifically designed for such images. Summary of the Invention

[0004] To address the problem of high image noise, this invention provides an image noise reduction method, a system computer device, and a storage medium.

[0005] To achieve the above objectives, the present invention provides an image denoising method, comprising: Satellite image samples of raw open-pit coal and rock were collected and processed into grayscale images.

[0006] The noise type and distribution characteristics of the grayscale image are statistically analyzed. Based on the noise type and distribution characteristics, the grayscale image is initially denoised to obtain an initial denoised image. The low-intensity noise signals in the initial denoised image are removed to obtain an enhanced denoised image. The gradient magnitude of each pixel in the enhanced denoised image is calculated to obtain a gradient magnitude image. The adaptive parameters are calculated using the gradient magnitude image to obtain an adaptive parameter matrix.

[0007] Set the initial value of the optimal estimated image, and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; count the energy change during the iteration process, and stop the iteration when the energy change is less than a set threshold to obtain the optimal denoised image.

[0008] Preferably, the gradient magnitude of each pixel in the enhanced and denoised image is calculated using the weighted Sobel gradient operator to obtain a gradient magnitude image, including: setting the weighted Sobel gradient operator along... x Gradient filters in the axial direction and along y Gradient filters in the axial direction ,use and Calculate the enhancement and denoising of the image in ( x , y gradient magnitude at ) The gradient magnitude expression is: ; In the formula, For convolution operations, F 2. To enhance the noise-reduced image.

[0009] Preferably, the step of calculating the adaptive parameters using the gradient magnitude image to obtain the adaptive parameter matrix includes: ; In the formula ， For convolution operations, p ( x , y ) is the adaptive parameter matrix P In pixels ( x , y The element at position ) p ( x , y The value range of ) is [1,2]; in the gradient magnitude image, when p ( x , y When it approaches 1, For the smooth region of the gradient magnitude image; when p ( x , y When it approaches 2, The edge region of the gradient magnitude image; for and exist Edge detection values ​​at the location.

[0010] Preferably, an adaptive generalized global variational noise reduction model is used. E ( u The optimal estimated image is iteratively updated step by step to obtain the optimal denoised image, and the adaptive generalized global variational denoising model is used. E ( u The adaptive generalized global variational denoising model is constructed from the gradient magnitude image and the adaptive parameter matrix. E ( u The expression is: ; In the formula, This is a gradient magnitude image. The optimal denoised image. For based on The calculated value of the regularization term, To ensure the accuracy of the values, These are the weight parameters.

[0011] Preferably, the step of acquiring satellite image samples of the original open-pit coal and rock and processing the satellite images into grayscale images includes: converting the original open-pit coal and rock satellite images into TIFF format and converting them to grayscale.

[0012] Preferably, the step of statistically analyzing the noise type and noise distribution characteristics of the grayscale image, and performing preliminary noise reduction on the grayscale image based on the noise type and noise distribution characteristics to obtain a preliminary denoised image includes: dividing the grayscale image into low-noise regions and high-noise regions based on the noise type and noise distribution characteristics; applying mean filtering to the low-noise regions and median filtering to the high-noise regions to obtain the image after preliminary noise reduction.

[0013] Preferably, removing low-intensity noise signals from the initial denoised image to obtain an enhanced denoised image includes: using a Gaussian-Laplace joint transform to remove low-intensity noise signals from the initial denoised image to obtain an enhanced denoised image.

[0014] The present invention also provides an image noise reduction system, comprising: The sample acquisition module is used to acquire satellite image samples of raw open-pit coal and rock, and process the satellite images into grayscale images.

[0015] The image processing module is used to statistically analyze the noise type and noise distribution characteristics of the grayscale image, perform preliminary noise reduction on the grayscale image based on the noise type and noise distribution characteristics to obtain a preliminary noise-reduced image; remove low-intensity noise signals from the preliminary noise-reduced image to obtain an enhanced noise-reduced image; calculate the gradient magnitude of each pixel in the enhanced noise-reduced image to obtain a gradient magnitude image; and use the gradient magnitude image to calculate adaptive parameters to obtain an adaptive parameter matrix.

[0016] The optimal denoised image finding module is used to set the initial value of the optimal estimated image, and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; it counts the energy change during the iteration process, and stops the iteration when the energy change is less than a set threshold, thus obtaining the optimal denoised image. 。

[0017] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the image noise reduction methods.

[0018] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the image denoising system method.

[0019] The image noise reduction method provided by this invention has the following beneficial effects: First, precise denoising is performed on the acquired open-pit coal and rock images. Then, the gradient magnitude of each pixel in the image is calculated to obtain a gradient magnitude image, which in turn yields an adaptive parameter matrix. The optimal estimated image is then iteratively updated using the gradient magnitude image and the adaptive parameter matrix to obtain the optimal denoised image. This method effectively solves the problems of incomplete denoising and severe detail loss in traditional methods. It can adapt to different regions and noise types, preserving rich detail information in the coal and rock images while ensuring noise removal. It maximizes denoising while retaining the original image information, providing high-quality image data support for accurate decision-making in open-pit mining. Attached Figure Description

[0020] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an image noise reduction method according to an embodiment of the present invention; Figure 2 The original image is from an embodiment of the present invention; Figure 3 This is a diagram showing the results of the generalized global variational processing in an embodiment of the present invention; Figure 4 The diagram shows the adaptive generalized global variational processing results of an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0023] This invention provides an image noise reduction method, specifically as follows: Figure 1 As shown, it includes: S1. Collect satellite image samples of raw open-pit coal and rock, and process the satellite images into grayscale images.

[0024] Using Google remote sensing satellites to acquire satellite remote sensing images of open-pit mines at a specific time, the obtained data is relatively detailed. The image format is uniformly converted into a standard format that is easy to process, such as TIFF format, and the images are converted to grayscale to reduce the amount of data and speed up subsequent processing.

[0025] S2. Statistically analyze the noise type and noise distribution characteristics of the grayscale image, and perform preliminary noise reduction on the grayscale image according to the noise type and noise distribution characteristics to obtain a preliminary noise-reduced image; remove the low-intensity noise signals in the preliminary noise-reduced image to obtain an enhanced noise-reduced image; calculate the gradient magnitude of each pixel in the enhanced noise-reduced image to obtain a gradient magnitude image; use the gradient magnitude image to calculate adaptive parameters to obtain an adaptive parameter matrix.

[0026] Due to limitations in satellite mapping and imaging systems at specific times, as well as the presence of interference signals during image transmission, the actual satellite remote sensing images of coal and rock in open-pit mines contain a large amount of non-native noise information. This noise hinders researchers and decision-makers from extracting and understanding image features. Therefore, researching noise reduction methods for coal and rock images to improve their quality is of significant academic value and importance for enhancing the overall optimization and processing of open-pit mine coal and rock images. This study uses statistical methods to analyze the noise characteristics of the grayscale images, calculates the grayscale histogram, and preliminarily determines the noise type based on the histogram's distribution. Simultaneously, the local variance method is used to evaluate the noise intensity in different regions of the image, determining the non-uniformity of noise distribution within the image.

[0027] The goal of open-pit coal and rock image denoising is to reduce or remove noise information in noisy coal and rock images to obtain an image of coal and rock distribution that is as close as possible to the actual situation. Since absolutely original satellite remote sensing images of coal and rock are usually unavailable, appropriate methods are needed to optimize and denoise the collected images. Traditional methods for digital image denoising and restoration include mean-based denoising, frequency-domain filtering, and wavelet transform. A common drawback of these traditional methods is that they also damage edge information in the image, such as texture, while denoising the image. This edge information is crucial for representing the distribution characteristics of coal and rock in coal and rock images. To improve the denoising effect of coal and rock images while effectively preserving image edge information, an adaptive generalized global variational coal and rock image denoising model and its gradient calculation method, along with a filtering method based on improved wavelet transform, are employed.

[0028] The noise types and distribution characteristics of grayscale images are statistically analyzed. Preliminary denoising is then performed on the grayscale images based on these noise types and distribution characteristics, with adaptive filtering algorithms selected for different regions. For regions with low noise intensity and relatively uniform distribution, mean filtering is used to quickly smooth the noise with a small filtering window. For regions with high noise intensity and a large amount of salt-and-pepper noise, median filtering is used to remove salt-and-pepper noise spikes due to the robustness of the median. Adaptive weighting coefficients are designed to dynamically adjust the filtering weights based on the noise estimates within the pixel neighborhood. This ensures that the filtering process removes noise while avoiding over-smoothing of image edges and details, resulting in the preliminarily denoised image.

[0029] A Gaussian filter is used to preprocess a noisy image to remove a small portion of the noise and reduce the possibility of mistaking noise for false edges, resulting in an enhanced and denoised image. The selection of this filter is related to the signal-to-noise ratio (SNR) of the noisy image, such as using a Gaussian-Laplace joint transform.

[0030] The adaptive generalized global variational open-pit coal and rock image denoising model is based on the harmonic model, global variational model, and generalized global variational model. It replaces the original simple Roberts gradient with a weighted Sobel gradient, which has a better edge-preserving effect, to enhance the magnitude and direction of the gradient in the original noisy open-pit coal and rock image. The preprocessing of the noisy coal and rock image uses a Gaussian-Laplace joint transform to ensure that edge information is preserved while filtering.

[0031] The Generalized Total Variation Denoising Model (GTV model) is a widely used denoising technique in image processing and computer vision. Based on the variational method, it transforms the image denoising problem into a minimization problem of an energy functional. It achieves the goal of removing noise while preserving image edges and details by constraining the gradient information of the image. Assuming the original image is... u ( x , y The noisy image is f ( x , y ),in( x , y () represents the coordinates of the image. The goal of the GTV model is to find an optimal image. u ( x , y ), making the energy functional E ( u Minimize, energy functional E ( u It typically consists of data fidelity items and regularization items.

[0032] Based on the L 1+p , the generalized total variation denoising model with 0 < p < 1 norm is an improvement and extension of the traditional generalized total variation denoising model. This model is also based on the variational method, which transforms the denoising problem into the minimization problem of an energy functional. Different from the traditional GTV model, it uses the L 1+p (0 < p < 1) norm in the regularization term to constrain the gradient of the image. Let the noisy image be f ( x , y ), and the original image to be solved is u ( x , y ). Its energy functional E (u) is usually composed of a data fidelity term and a regularization term based on the L 1+p norm. The data fidelity term adopts a common form, such as , to ensure the similarity between the denoised image and the original noisy image to a certain extent. The regularization term adopts , where represents the gradient magnitude of the image, and 0 < p < 1. This non-convex L 1+p norm regularization term has some unique properties compared with the traditional L1 norm or L2 norm regularization term. When 0 < p < 1, the L 1+p norm can promote the sparsity of the image gradient more effectively than the L1 norm, enabling the model to more accurately retain important features such as edges and details of the image while removing noise, and having a more significant denoising effect on images with complex textures and rich details.

[0033] Based on the L 1+p , 0 < p < 1 norm, the generalized total variation denoising model can solve the problem of the appearance of false edges in the image and retain edge information while denoising. However, the selection of p in this model is very sensitive. The adjustment of the parameters in this model will have a certain impact on the computational complexity. When approaching 0 or 1, the calculation is relatively stable, but when p takes a middle value, due to the enhanced non-convexity of the regularization term, the solution process may become more complex, requiring more iteration times and more refined adjustments, thus increasing the calculation time and complexity.

[0034] To solve this problem, an adaptive generalized total variation image denoising model is constructed. In the model, p = p(x, y) is related to each pixel point in the image. The values of p for different pixel points are different, so that the L 1+p norm can be adaptively selected according to the characteristics of each pixel point.

[0035] Using the weighted sobel gradient operator, calculate the gradient magnitude of each pixel point in the enhanced denoising image to obtain the gradient magnitude image Using gradient magnitude images Calculate adaptive parameters p ( x , y ), thus obtaining the adaptive parameter matrix P Specifically, the transverse gradient filter of the weighted Sobel gradient operator is set as follows: and longitudinal gradient filter are Calculate enhanced and denoised images F 2 in ( x , y gradient magnitude at ) The gradient magnitude expression is:

[0036] ; In the formula, For convolution operations, using and right F 2. Perform edge detection.

[0037] use Calculate adaptive parameters p ( x , y ), thus obtaining the adaptive parameter matrix, expressed as: ; In the formula ， For convolution operations, p ( x , y The value range of ) is [1,2], used to control the adaptive parameters of each pixel. The edge region of the image, p ( x , y The value approaches 1. Smooth region, p ( x , y It approaches 2. for At pixel The value at that location, for and exist Edge detection values ​​at the location, Gaussian filter , where >0.

[0038] For image edges, Very large, when When it approaches infinity, =1, selecting a global variational model that is good at preserving edge information. The model is approximately equivalent to a global variational model (anisotropic diffusion) that is good at preserving edge information. For regions far from the image edges, Very small, when When it approaches 0, =1, selecting a harmonic model that better preserves smoothness; the model approximates a harmonic model (isotropic diffusion) that better preserves smoothness. Therefore, this model can adaptively select parameters. The value of is determined so that the correct model is selected in different regions.

[0039] S4. Set the initial value of the optimal estimated image, and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; count the energy change during the iteration process, and stop the iteration when the energy change is less than a set threshold to obtain the optimal denoised image.

[0040] An adaptive generalized global variational noise reduction model is proposed. E ( u The model expression is: ; In the formula, This is a gradient magnitude image. The optimal denoised image. For based on The calculated value of the regularization term, To ensure the accuracy of the values, These are the weight parameters.

[0041] Based on the gradient information of each pixel in the noisy image, the adaptive global variational image denoising model can adaptively select the parameters that determine the smoothing strength in the denoising model. To achieve the effect of "noise reduction and edge preservation", E ( u Iterative updates u ,when E ( u When the change in ) is less than the threshold, the optimal denoised image is obtained. u .

[0042] To verify the feasibility of the above scheme, Mine images of the coal seams in a large open-pit coal mine (Haizhou Open-pit Mine) were captured using Google Remote Sensing Satellite according to predetermined satellite image acquisition parameters. The image information is as follows: resolution and size are 256×256, and grayscale levels are 256. The original noisy image is shown below. Figure 2 Assuming the Gaussian filter has a window size of 3×3 and a variance of 0.2, in the generalized global variational model... p The initial value is set to 1.3. See the image processing example for the generalized global variational model. Figure 3 Next, an adaptive generalized global variational filtering model was applied, and the results are shown below. Figure 4 Compared to the original image, the clear coal and rock image shows a significant reduction in noise, and the coal and rock texture and boundaries are clearly distinguishable, providing a strong basis for the mining planning of this working face.

[0043] Practical application cases in open-pit mines demonstrate that the noise reduction method of this invention has wide applicability and stability, and can effectively solve the problem of noise interference in coal and rock images in open-pit mines.

[0044] Based on the same inventive concept, the present invention also provides an image noise reduction system, comprising: The sample acquisition module is used to acquire satellite image samples of raw open-pit coal and rock, and process the satellite images into grayscale images.

[0045] The image processing module is used to statistically analyze the noise type and noise distribution characteristics of the grayscale image, perform preliminary noise reduction on the grayscale image based on the noise type and noise distribution characteristics to obtain a preliminary noise-reduced image; remove low-intensity noise signals from the preliminary noise-reduced image to obtain an enhanced noise-reduced image; calculate the gradient magnitude of each pixel in the enhanced noise-reduced image to obtain a gradient magnitude image; and use the gradient magnitude image to calculate adaptive parameters to obtain an adaptive parameter matrix.

[0046] The optimal denoised image finding module is used to set the initial value of the optimal estimated image, and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; it counts the energy change during the iteration process, and stops the iteration when the energy change is less than a set threshold, thus obtaining the optimal denoised image. 。

[0047] This invention also provides a computer device, which, at the hardware level, includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the image noise reduction method provided above.

[0048] The present invention also provides a computer-readable storage medium storing a computer program that can be used to perform the image noise reduction method described above.

[0049] For specific limitations regarding the computational system of the image denoising method, please refer to the limitations of the image denoising method mentioned above, which will not be repeated here. Each module in the above-mentioned image denoising method system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0050] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, the above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0051] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An image denoising method, characterized in that, The method includes: Satellite image samples of raw open-pit coal and rock were collected, and the satellite images were processed into grayscale images; The noise type and distribution characteristics of the grayscale image are statistically analyzed. Based on the noise type and distribution characteristics, the grayscale image is initially denoised to obtain an initial denoised image. Low-intensity noise signals are removed from the initial denoised image to obtain an enhanced denoised image. The gradient magnitude of each pixel in the enhanced denoised image is calculated to obtain a gradient magnitude image. Adaptive parameters are calculated using the gradient magnitude image to obtain an adaptive parameter matrix. Set the initial value of the optimal estimated image, and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; count the energy change during the iteration process, and stop the iteration when the energy change is less than a set threshold to obtain the optimal denoised image.

2. The image denoising method according to claim 1, characterized in that, The gradient magnitude of each pixel in the enhanced and denoised image is calculated using the weighted Sobel gradient operator to obtain a gradient magnitude image, including: setting the weighted Sobel gradient operator along... x Gradient filters in the axial direction and along y Gradient filters in the axial direction ,use and Calculate the enhancement and denoising of the image in ( x , y gradient magnitude at ) The gradient magnitude expression is: ； In the formula, For convolution operations, F 2. To enhance the noise-reduced image.

3. The image denoising method according to claim 2, characterized in that, The process of calculating adaptive parameters using gradient magnitude images to obtain an adaptive parameter matrix includes: ； In the formula ， For convolution operations, p ( x , y ) is the adaptive parameter matrix P In pixels ( x , y The element at position ) p ( x , y The value range of ) is [1,2]; in the gradient magnitude image, when p ( x , y When it approaches 1, For the smooth region of the gradient magnitude image; when p ( x , y When it approaches 2, The edge region of the gradient magnitude image; for and exist Edge detection values ​​at the location.

4. The image denoising method according to claim 3, characterized in that, Adaptive generalized global variational noise reduction model E ( u The optimal estimated image is iteratively updated step by step to obtain the optimal denoised image, and the adaptive generalized global variational denoising model is used. E ( u The adaptive generalized global variational denoising model is constructed from the gradient magnitude image and the adaptive parameter matrix. E ( u The expression is: ； In the formula, This is a gradient magnitude image. The optimal denoised image. For based on The calculated value of the regularization term, To ensure the accuracy of the values, These are the weight parameters.

5. The image denoising method according to claim 1, characterized in that, The process of acquiring satellite image samples of raw open-pit coal and rock and processing the satellite images into grayscale images includes: converting the raw open-pit coal and rock satellite images into TIFF format and converting them to grayscale.

6. The image denoising method according to claim 1, characterized in that, The process involves statistically analyzing the noise type and distribution characteristics of the grayscale image, and performing preliminary noise reduction on the grayscale image based on the noise type and distribution characteristics to obtain a preliminary denoised image. This includes: dividing the grayscale image into low-noise regions and high-noise regions based on the noise type and distribution characteristics; applying mean filtering to the low-noise regions and median filtering to the high-noise regions to obtain the image after preliminary noise reduction.

7. The image denoising method according to claim 1, characterized in that, The step of removing low-intensity noise signals from the initial denoised image to obtain an enhanced denoised image includes: using a Gaussian-Laplace joint transform to remove low-intensity noise signals from the initial denoised image to obtain an enhanced denoised image.

8. An image noise reduction system, characterized in that, include: The sample acquisition module is used to acquire satellite image samples of raw open-pit coal and rock, and process the satellite images into grayscale images; The image processing module is used to statistically analyze the noise type and noise distribution characteristics of the grayscale image, and to perform preliminary noise reduction on the grayscale image based on the noise type and noise distribution characteristics to obtain a preliminary noise-reduced image; Remove low-intensity noise signals from the initial denoised image to obtain an enhanced denoised image; Calculate the gradient magnitude of each pixel in the enhanced and denoised image to obtain a gradient magnitude image; The adaptive parameters are calculated using the gradient magnitude image, resulting in the adaptive parameter matrix; The optimal denoised image finding module is used to set the initial value of the optimal estimated image and iteratively update the optimal estimated image step by step using the gradient magnitude image and the adaptive parameter matrix; The iteration process is monitored for energy changes. When the energy change is less than a set threshold, the iteration stops, and the optimal denoised image is obtained. 。 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method steps of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method steps as described in any one of claims 1 to 7.

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