Image processing method for cladding effect on surface of base material

Abstract

The invention provides a base material surface cladding effect image processing method, which belongs to the field of image processing, and is used for processing an input base material surface cladding effect image through a frequency domain decomposition and adaptive fusion strategy and a high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum. According to the method, the feature extraction efficiency is high, the definition of the enhanced image is high, detail expression is rich, and the method is suitable for the real-time processing capability of a complex industrial scene; and reliable technical support can be provided for automatic detection and evaluation of the cladding quality of the surface of the base material.

Description

Technical Field

[0001] The invention belongs to the field of image processing, and in particular relates to a method for processing an image of a substrate surface cladding effect. Background Art

[0002] Substrate surface cladding technology is a commonly used surface treatment method. It uses laser to clad a layer of specific material on the surface of the substrate to improve the wear resistance, corrosion resistance and other properties of the workpiece. It has been widely used in aerospace, automobile manufacturing, metallurgy and mechanical processing. However, due to the complexity of the cladding process, which involves high-temperature heating, material melting, cooling and other links, there are often some defects on the surface of the substrate after cladding, such as pores, cracks, uneven coating, cladding thickness deviation, etc. These defects directly affect the quality and service life of the cladding layer, and may even cause the workpiece to fail. Therefore, the detection and quality control of the cladding surface is particularly important.

[0003] At present, the detection of cladding effect on the substrate surface mainly relies on traditional non-destructive testing methods, such as ultrasonic testing, X-ray testing and optical microscope testing. Although these methods can provide a certain degree of quality assessment, the detection efficiency is relatively low and the information obtained is limited. With the rapid development of artificial intelligence and image processing technology, the combination of multimodal information (such as RGB images and depth images) has shown significant advantages in the field of industrial inspection, especially in the detection of cladding effect on the substrate surface. RGB images can provide surface texture information, while depth images can capture tiny three-dimensional morphological changes. The combination of the two can more accurately process the substrate surface image. This method not only solves the problem of data scarcity and low detection efficiency, but also can achieve high-precision defect detection in complex industrial environments, thereby providing strong support for improving the reliability and stability of the substrate cladding process. Summary of the invention

[0004] The present invention provides a method for processing an image of a substrate surface cladding effect, aiming to generate an enhanced image of the substrate surface cladding effect by fusing an RGB image with a depth modal feature, thereby improving the accuracy and reliability of evaluating the substrate surface cladding quality, and comprising the following steps.

[0005] S1. Collect substrate surface cladding effect images containing RGB and depth information to create a substrate surface cladding effect image dataset.

[0006] S2. Construct frequency domain decomposition and adaptive fusion strategy, including: using discrete wavelet transform to decompose the substrate surface cladding effect image into low-frequency and multi-directional high-frequency sub-bands, introducing learnable parameters to adaptively fuse RGB and depth information in each frequency band, and using inverse wavelet transform to reconstruct the substrate surface cladding effect fusion feature map.

[0007] S3. Construct a high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum to extract features from the fused feature map of the substrate surface cladding effect, including: converting the fused feature map of the substrate surface cladding effect into grayscale and expanding it into a one-dimensional sequence, constructing the phase space trajectory using Takens embedding theory, and then generating the corresponding recursive quantization spectrum index based on the preset threshold, so as to construct the global feature vector of the substrate surface cladding effect.

[0008] S4. Construct a module for generating an enhanced image of the cladding effect on the substrate surface, including: calculating a difference image of a fusion feature map of the cladding effect on the substrate surface, designing an adaptive enhancement strength adjustment factor, using the adaptive enhancement strength adjustment factor and the global feature vector of the cladding effect on the substrate surface to adjust the enhancement strength of the local variation, and generating an enhanced image of the cladding effect on the substrate surface.

[0009] S5. Construct an image processing model for enhancing the cladding effect on the substrate surface, and use a dataset of images of the cladding effect on the substrate surface for training.

[0010] Preferably, in S1, an industrial depth camera is used to take a picture of the surface of the substrate to be clad by laser to obtain an RGB image of the cladding effect on the surface of the substrate. And the depth image of the cladding effect on the substrate surface , the obtained RGB image of the substrate surface cladding effect and the depth image of the substrate surface cladding effect are labeled and made into a substrate surface cladding effect image dataset.

[0011] Preferably, in S2, the input image includes an RGB image of the cladding effect on the substrate surface And the depth image of the cladding effect on the substrate surface The specific process includes: S21, perform discrete wavelet transform on the input image, Each channel Perform discrete wavelet transform, the specific process is: , in express The low frequency subband on channel c, , and Respectively High frequency sub-bands in horizontal, vertical and diagonal directions on channel c, represents discrete wavelet transform, express The image matrix on channel c, for Perform discrete wavelet transform, the specific process is: , in express The low frequency subband of , and Respectively high frequency sub-bands in horizontal, vertical and diagonal directions; S22, introduce learnable parameters to adjust the fusion ratio of RGB and depth information in each frequency band. The specific process is as follows: , in is the learnable parameter of the low-frequency subband, , used to control the contribution ratio of low-frequency information of each channel of RGB image, is the fused low-frequency subband, , in is the learnable parameter of the high-frequency sub-band in the horizontal direction, , used to control the contribution ratio of each channel of the RGB image in the horizontal high-frequency component, is the high-frequency sub-band in the horizontal direction after fusion, , in is the learnable parameter of the high-frequency sub-band in the vertical direction, , used to control the contribution ratio of each channel of the RGB image in the vertical high-frequency component, is the high-frequency sub-band in the vertical direction after fusion, , in is the learnable parameter of the high-frequency subband in the diagonal direction, , used to control the contribution ratio of each channel of the RGB image to the diagonal high-frequency components, is the high-frequency sub-band in the diagonal direction after fusion; S23, reconstruct the fused characteristic map of the cladding effect on the substrate surface by inverse wavelet transforming the fused frequency bands , the specific process is: , in Represents inverse wavelet transform.

[0012] Preferably, in S2, the information of the RGB and depth images of the substrate surface cladding effect is processed separately in different frequency bands based on the strategy of frequency domain decomposition and adaptive fusion, and dynamic fusion is achieved by introducing learnable parameter adaptation, which can make fuller use of the complementary advantages of the two modes in global structure and local details. After DWT decomposition, not only the low-frequency overall information of the image can be separated, but also the high-frequency details can be captured finely, which is crucial for the detection of tiny defects in the substrate surface cladding effect image. At the same time, the RGB and depth modal information are more meticulously integrated, providing a richer and more accurate feature basis for subsequent image enhancement and defect detection.

[0013] Preferably, in said S3, the substrate surface cladding effect is integrated with the characteristic map Convert to grayscale image of cladding effect on substrate surface , the specific process is: , in , and They are the fusion characteristic diagrams of the substrate surface cladding effect. exist The values ​​of the three RGB channels; Grayscale image of the cladding effect on the substrate surface Expand into a one-dimensional sequence in behavioral order , the specific process is: , Where H and W are grayscale images of the cladding effect on the substrate surface. height and width; According to Takens theory, we select the embedding dimension m and the time delay r to construct the reconstructed phase space vector , No. The phase space vector corresponding to the moment The specific construction process is: , in represents the matrix transpose, , and Respectively represent , and One-dimensional sequence The value in Represents the reconstructed phase space vector Size; Using the reconstructed phase space vector Constructing a recursive matrix , the specific process is: , in represents the Euclidean distance, represents the distance threshold, and Respectively represent and The phase space vector corresponding to each moment is , represents the Heaviside step function, , From the recursive matrix Extract recursive quantization spectrum indicators, including recursive , Certainty ,entropy and the average diagonal length , the specific process is: , in Represents the recursive matrix The dimension of , in Represents a recursive matrix The length of the diagonal line, The length of the diagonal is The number of strips, Indicates the minimum length threshold of the diagonal line, , in represents the log function, , Then the extracted recursive quantization spectrum indexes are combined to construct the global feature vector of the substrate surface cladding effect , the specific process is: .

[0014] Preferably, in S3, the high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum reveals the inherent repetitive pattern and complexity of the substrate surface cladding effect image from the perspective of system dynamic evolution, and provides an intuitive and physically meaningful feature description. Through phase space reconstruction and recursive matrix construction, it not only captures the local detail repeatability of the substrate surface cladding effect image, but also reflects the dynamic changes of the global structure. This strategy relies on mathematically rigorous nonlinear dynamics and recursive quantization analysis methods. The recursive quantization index has high robustness to noise and local disturbances, can accurately extract image features under complex backgrounds, and is suitable for the detection of complex substrate surface cladding effect images.

[0015] Preferably, in said S4, the difference image of the cladding effect fusion feature map of the substrate surface is calculated , It reflects the detailed information of local changes in the fusion feature map of the substrate surface cladding effect. The specific process is as follows: , in , and Respectively represent the fusion feature map of the substrate surface cladding effect on the c channel , and The pixel value at the position; Constructing an adaptive enhancement intensity regulator , the specific process is: , in , , and Respectively represent the recursive matrix Extract the recursion amount, certainty, entropy and average diagonal length of the recursion, It represents the adjustment coefficient of the global enhancement strength, which controls the overall enhancement strength. Its value range is [0,10]. Represents the adjustment coefficient of local details, controls the influence of the difference image on the enhancement strength, and its value range is [0,2]. This ensures that the enhancement effect will not be too strong in areas with small local changes; The enhanced intensity of the local variation is adjusted by using the adaptive enhanced intensity adjustment factor and the global feature vector of the substrate surface cladding effect to generate an enhanced image of the substrate surface cladding effect. , the specific process is: , in Represents the global feature vector of the cladding effect on the substrate surface.

[0016] Preferably, in S4, by calculating the difference image of the fusion feature map of the substrate surface cladding effect and combining it with the global feature vector of the substrate surface cladding effect, the gain of the image is adaptively adjusted to enhance the key details of the substrate surface, while controlling the intensity of the enhancement to avoid excessive enhancement of background noise or unnecessary areas, so that surface defects and details can be effectively highlighted and the overall structure of the image is not destroyed. The enhancement effect is natural and has high visual and analytical accuracy, thereby improving the quality of the substrate surface cladding effect image.

[0017] Preferably, in S5, a substrate surface cladding effect enhanced image processing model is constructed, and the input substrate surface cladding effect image is processed through frequency domain decomposition and adaptive fusion strategy, and high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum, and a substrate surface cladding effect enhanced image generation module is used to generate a substrate surface cladding effect enhanced image and output it.

[0018] Compared with the prior art, the present invention has the following technical effects: by adaptively fusing RGB and depth information in the frequency domain, rich textures and colors are retained while geometric changes are captured; high-order dynamic features are extracted using phase space evolution and recursive quantization spectra, greatly improving the ability to recognize uneven cladding layers and defects; difference images are combined with global features to achieve adaptive enhancement of key areas and suppress background noise; adaptive adjustment factors are introduced to balance local and overall structures, making the enhancement effect natural; the integrated processing flow has strong robustness and versatility, which is convenient for industrial detection and subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a substrate surface cladding effect image processing method provided by the present invention.

[0020] Figure 2 This is a process diagram of the image processing of the substrate surface cladding effect provided by the present invention.

[0021] Figure 3 It is a processing diagram of the substrate surface cladding effect enhancement image generation module provided by the present invention.

[0022] Figure 4 This is a comparison diagram of image processing of substrate surface cladding effect in one embodiment provided by the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] See also Figures 1 to 4 The present invention provides a method for processing an image of a substrate surface cladding effect, which aims to generate an enhanced image of the substrate surface cladding effect by fusing an RGB image with a depth modal feature, thereby improving the accuracy and reliability of evaluating the substrate surface cladding quality, and includes the following steps.

[0025] S1. Collect substrate surface cladding effect images containing RGB and depth information to create a substrate surface cladding effect image dataset.

[0026] Furthermore, in S1, an industrial depth camera is used to take a picture of the surface of the substrate using laser cladding to obtain an RGB image of the cladding effect on the surface of the substrate. And the depth image of the cladding effect on the substrate surface , a total of 3000 images, the obtained RGB images of substrate surface cladding effect and depth images of substrate surface cladding effect are labeled using Labelimg to make a substrate surface cladding effect image dataset, which is divided into training set and verification set according to 7:3.

[0027] S2. Construct frequency domain decomposition and adaptive fusion strategy, including: using discrete wavelet transform to decompose the substrate surface cladding effect image into low-frequency and multi-directional high-frequency sub-bands, introducing learnable parameters to adaptively fuse RGB and depth information in each frequency band, and using inverse wavelet transform to reconstruct the substrate surface cladding effect fusion feature map.

[0028] Furthermore, in S2, the input image includes an RGB image of the cladding effect on the substrate surface. And the depth image of the cladding effect on the substrate surface The specific process includes: S21, perform discrete wavelet transform on the input image, Each channel Perform discrete wavelet transform, the specific process is: , in express The low frequency subband on channel c, , and Respectively High frequency sub-bands in horizontal, vertical and diagonal directions on channel c, represents discrete wavelet transform, express The image matrix on channel c, for Perform discrete wavelet transform, the specific process is: , in express The low frequency subband of , and Respectively high frequency sub-bands in horizontal, vertical and diagonal directions; S22, introduce learnable parameters to adjust the fusion ratio of RGB and depth information in each frequency band. The specific process is as follows: , in is the learnable parameter of the low-frequency subband, , used to control the contribution ratio of low-frequency information of each channel of RGB image, is the fused low-frequency subband, , in is the learnable parameter of the high-frequency sub-band in the horizontal direction, , used to control the contribution ratio of each channel of the RGB image in the horizontal high-frequency component, is the high-frequency sub-band in the horizontal direction after fusion, , in is the learnable parameter of the high-frequency sub-band in the vertical direction, , used to control the contribution ratio of each channel of the RGB image in the vertical high-frequency component, is the high-frequency sub-band in the vertical direction after fusion, , in is the learnable parameter of the high-frequency subband in the diagonal direction, , used to control the contribution ratio of each channel of the RGB image to the diagonal high-frequency components, is the high frequency sub-band in the diagonal direction after fusion. In this embodiment, , , and The initial values ​​of are all set to 0.5, and the gradient is updated by combining the loss function through back propagation. , , and The value of is limited to the range [0,1]; S23, reconstruct the fused characteristic map of the cladding effect on the substrate surface by inverse wavelet transforming the fused frequency bands , the specific process is: , in Represents inverse wavelet transform.

[0029] S3. Construct a high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum to extract features from the fused feature map of the substrate surface cladding effect, including: converting the fused feature map of the substrate surface cladding effect into grayscale and expanding it into a one-dimensional sequence, constructing the phase space trajectory using Takens embedding theory, and then generating the corresponding recursive quantization spectrum index based on the preset threshold, so as to construct the global feature vector of the substrate surface cladding effect.

[0030] Furthermore, in S3, the substrate surface cladding effect is integrated with the characteristic map Convert to grayscale image of cladding effect on substrate surface , the specific process is: , in , and They are the fusion characteristic diagrams of the substrate surface cladding effect. exist The values ​​of the three RGB channels; Grayscale image of the cladding effect on the substrate surface Expand into a one-dimensional sequence in behavioral order , the specific process is: , Where H and W are grayscale images of the cladding effect on the substrate surface. height and width; According to Takens theory, we select the embedding dimension m and the time delay r to construct the reconstructed phase space vector ,The selection of the embedding dimension m is to ensure that the reconstructed phase space can fully capture the dynamic information of the system, the selection range of m is [5, 10], and it is selected by the FNN method. ,The selection of time delay r determines the temporal relationship between points in the phase space, and the selection range of r is [5, 15], and it is selected by the autocorrelation function, which is determined by calculating the autocorrelation function and selecting its first zero crossing point. ,In this embodiment, the initial value of m is set to 8, and the initial value of r is set to 10, which can effectively capture the texture and structural characteristics of the cladding effect image on the substrate surface. The phase space vector corresponding to the moment The specific construction process is: , in represents the matrix transpose, , and Respectively represent , and One-dimensional sequence The value in Represents the reconstructed phase space vector Size; Using the reconstructed phase space vector Constructing a recursive matrix , the specific process is: , in represents the Euclidean distance, Indicates the distance threshold, which controls the image similarity judgment. In order to capture the subtle changes in the cladding effect image on the substrate surface, in this embodiment Set to 0.1, and Respectively represent and The phase space vector corresponding to each moment is , represents the Heaviside step function, , From the recursive matrix Extract recursive quantization spectrum indicators, including recursive , Certainty ,entropy and the average diagonal length , the specific process is: , in Represents the recursive matrix The dimension of , in Represents a recursive matrix The length of the diagonal line, The length of the diagonal is The number of strips, Indicates the minimum length threshold of the diagonal line, , in represents the log function, , Then the extracted recursive quantization spectrum indexes are combined to construct the global feature vector of the substrate surface cladding effect , the specific process is: .

[0031] S4. Construct a module for generating an enhanced image of the cladding effect on the substrate surface, including: calculating a difference image of a fusion feature map of the cladding effect on the substrate surface, designing an adaptive enhancement strength adjustment factor, using the adaptive enhancement strength adjustment factor and the global feature vector of the cladding effect on the substrate surface to adjust the enhancement strength of the local variation, and generating an enhanced image of the cladding effect on the substrate surface.

[0032] Furthermore, in S4, the difference image of the cladding effect fusion feature map of the substrate surface is calculated. , It reflects the detailed information of local changes in the fusion feature map of the substrate surface cladding effect. The specific process is as follows: , in , and Respectively represent the fusion feature map of the substrate surface cladding effect on the c channel , and The pixel value at the position; Constructing an adaptive enhancement intensity regulator , the specific process is: , in , , and Respectively represent the recursive matrix Extract the recursion amount, certainty, entropy and average diagonal length of the recursion, It represents the adjustment coefficient of the global enhancement strength, which controls the overall enhancement strength. Its value range is [0,10]. Represents the adjustment coefficient of local details, controls the influence of the difference image on the enhancement strength, and its value range is [0,2]. It is ensured that the enhancement effect will not be too strong in the area with small local changes. In this embodiment, The initial value of is set to 1. The initial value is set to 0.5, and the loss function renew and The value of The specific formula is: ; The enhanced intensity of the local variation is adjusted by using the adaptive enhanced intensity adjustment factor and the global feature vector of the substrate surface cladding effect to generate an enhanced image of the substrate surface cladding effect. , the specific process is: , in Represents the global feature vector of the cladding effect on the substrate surface.

[0033] S5. Construct an image processing model for enhancing the cladding effect on the substrate surface, and use a dataset of images of the cladding effect on the substrate surface for training.

[0034] Furthermore, in S5, a substrate surface cladding effect enhanced image processing model is constructed, and the input substrate surface cladding effect image is processed through frequency domain decomposition and adaptive fusion strategy, and high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum, and a substrate surface cladding effect enhanced image generation module is used to generate a substrate surface cladding effect enhanced image and output it.

[0035] Furthermore, the image processing model for enhancing the cladding effect on the substrate surface was coded using the Pycharm application and Python language, using the Pytorch framework. The model input was trained using an image of the cladding effect on the substrate surface with a resolution of 640×640×3. The model was trained for 300 epochs, of which 50 epochs were frozen.

[0036] The above are only preferred embodiments of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for processing an image of a substrate surface cladding effect, characterized in that: The following steps are involved: S1, collecting substrate surface cladding effect images containing RGB and depth information, and preparing a substrate surface cladding effect image dataset; S2. Constructing a frequency domain decomposition and adaptive fusion strategy, including: using discrete wavelet transform to decompose the substrate surface cladding effect image into low-frequency and multi-directional high-frequency sub-bands, introducing learnable parameters to adaptively fuse RGB and depth information in each frequency band, and using inverse wavelet transform to reconstruct the substrate surface cladding effect fusion feature map; S3. Constructing a high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum to extract features from the fusion feature map of the substrate surface cladding effect, including: converting the fusion feature map of the substrate surface cladding effect into grayscale and expanding it into a one-dimensional sequence, constructing a phase space trajectory using Takens embedding theory, and then generating corresponding recursive quantization spectrum indicators based on a preset threshold, thereby constructing a global feature vector of the substrate surface cladding effect; S4, constructing a substrate surface cladding effect enhanced image generation module, including: calculating a substrate surface cladding effect fusion feature map difference image, designing an adaptive enhancement strength adjustment factor, using the adaptive enhancement strength adjustment factor and the substrate surface cladding effect global feature vector to adjust the enhancement strength of the local variation, and generating a substrate surface cladding effect enhanced image; S5. Construct an image processing model for enhancing the cladding effect on the surface of a substrate, and use the image data set of the cladding effect on the surface of the substrate for training.

2. According to claim 1, a substrate surface cladding effect image processing method is characterized in that: In S2, the input image includes an RGB image of the cladding effect on the substrate surface. And the depth image of the cladding effect on the substrate surface , the specific method is: S21, perform discrete wavelet transform on the input image, Each channel Perform discrete wavelet transform. The specific method is: ， in express The low frequency subband on channel c, , and Respectively High frequency sub-bands in horizontal, vertical and diagonal directions on channel c, represents discrete wavelet transform, express The image matrix on channel c, for Perform discrete wavelet transform. The specific method is: ， in express The low frequency subband of , and Respectively high frequency sub-bands in horizontal, vertical and diagonal directions; S22. Introduce learnable parameters to adjust the fusion ratio of RGB and depth information in each frequency band. The specific method includes: ， in is the learnable parameter of the low-frequency subband, , is the fused low-frequency subband, ， in is the learnable parameter of the high-frequency sub-band in the horizontal direction, , is the high-frequency sub-band in the horizontal direction after fusion, ， in is the learnable parameter of the high-frequency sub-band in the vertical direction, , is the high-frequency sub-band in the vertical direction after fusion, ， in is the learnable parameter of the high-frequency subband in the diagonal direction, , is the high-frequency sub-band in the diagonal direction after fusion; S23, reconstruct the fused characteristic map of the cladding effect on the substrate surface by inverse wavelet transforming the fused frequency bands , the specific method is: ， in Represents inverse wavelet transform.

3. A substrate surface cladding effect image processing method according to claim 2, characterized in that: In S3, the substrate surface cladding effect is integrated with the characteristic map Convert to grayscale image of cladding effect on substrate surface , the specific method is: ， in , and They are the fusion characteristic diagrams of the substrate surface cladding effect. exist The values ​​of the three RGB channels; Grayscale image of the cladding effect on the substrate surface Expand into a one-dimensional sequence in behavioral order , the specific method is: ， Where H and W are grayscale images of the cladding effect on the substrate surface. height and width; According to Takens theory, we select the embedding dimension m and the time delay r to construct the reconstructed phase space vector , No. The phase space vector corresponding to the moment The specific method of constructing is: ， in represents the matrix transpose, , and Respectively represent , and One-dimensional sequence The value in Represents the reconstructed phase space vector Size; Using the reconstructed phase space vector Constructing a recursive matrix , the specific method is: ， in represents the Euclidean distance, represents the distance threshold, and Respectively represent and The phase space vector corresponding to each moment is , represents the Heaviside step function, From the recursive matrix Extract recursive quantization spectrum indicators, including recursive , Certainty ,entropy and the average diagonal length , the specific method is: ， in Represents the recursive matrix The dimension of ， in Represents a recursive matrix The length of the diagonal line, The length of the diagonal is The number of strips, Indicates the minimum length threshold of the diagonal line, ， in represents the log function, ， Then the extracted recursive quantization spectrum indexes are combined to construct the global feature vector of the substrate surface cladding effect , the specific method is: 。 4. A substrate surface cladding effect image processing method according to claim 3, characterized in that: In S4, the cladding effect of the substrate surface is calculated by fusion of the feature map difference image , the specific method is: ， in , and Respectively represent the fusion feature map of the substrate surface cladding effect on the c channel , and The pixel value at the position; Constructing an adaptive enhancement intensity regulator , the specific method is: ， in , , and Respectively represent the recursive matrix Extract the recursion amount, certainty, entropy and average diagonal length of the recursion, represents the adjustment coefficient of the global enhancement strength, The adjustment coefficient representing the local details; The enhanced intensity of the local variation is adjusted by using the adaptive enhanced intensity adjustment factor and the global feature vector of the substrate surface cladding effect to generate an enhanced image of the substrate surface cladding effect. , the specific method is: ， in Represents the global feature vector of the cladding effect on the substrate surface.

5. A substrate surface cladding effect image processing method according to claim 4, characterized in that: In the S5, a substrate surface cladding effect enhanced image processing model is constructed, and the input substrate surface cladding effect image is processed through frequency domain decomposition and adaptive fusion strategy, and high-order dynamic feature extraction strategy based on phase space evolution and recursive quantization spectrum. The substrate surface cladding effect enhanced image generation module is used to generate and output the substrate surface cladding effect enhanced image.

Images