CN117649661B - Carbon nanotube preparation state image processing method - Google Patents
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- 239000002041 carbon nanotube Substances 0.000 title claims abstract description 26
- 229910021393 carbon nanotube Inorganic materials 0.000 title claims abstract description 26
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- 239000011159 matrix material Substances 0.000 claims abstract description 38
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Abstract
The invention discloses a carbon nanotube preparation state image processing method, which belongs to the technical field of image processing and comprises the following steps: s1, scanning the surface of a carbon nanotube sample by using a scanning electron microscope to obtain a surface image, and denoising the surface image to obtain a characteristic image; s2, determining state characteristic values of all pixel points in the characteristic image to obtain a state characteristic matrix of the characteristic image; s3, correcting the characteristic image according to the state characteristic matrix of the characteristic image to obtain a standard surface image, and finishing image processing. The method comprises the steps of carrying out denoising treatment on a surface image of a scanned carbon nanotube sample, extracting a state feature matrix representing pixel point features of the whole image, and obtaining the state feature matrix through convolution, feature calculation and the like; the invention also carries out color correction on the image according to the state characteristic matrix, ensures the definition of the carbon nanotube sample image, and further improves the accuracy of observation.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a carbon nanotube preparation state image processing method.
Background
The carbon nanotube is a nano-sized tubular structure formed by carbon atoms in a certain mode, has excellent physical properties such as high strength, high electrical conductivity, high thermal conductivity and the like, and can fully exert the advantages of the carbon nanotube and other materials by combining the carbon nanotube with the other materials, so that the performance of the materials is improved. The dispersion state of the carbon nanotubes is a key factor, and directly affects the bonding performance of the carbon nanotubes with other materials. The dispersion state of the carbon nanotubes is generally observed through an optical microscope, a scanning electron microscope, a transmission electron microscope and the like, so that the quality of the collected images of the carbon nanotubes directly determines the observation result, and how to improve the image definition is an urgent problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides a method for processing a carbon nanotube preparation state image.
The technical scheme of the invention is as follows: the carbon nanotube preparation state image processing method comprises the following steps:
s1, scanning the surface of a carbon nanotube sample by using a scanning electron microscope to obtain a surface image, and denoising the surface image to obtain a characteristic image;
s2, determining state characteristic values of all pixel points in the characteristic image to obtain a state characteristic matrix of the characteristic image;
s3, correcting the characteristic image according to the state characteristic matrix of the characteristic image to obtain a standard surface image, and finishing image processing.
Scanning electron microscopy is a common characterization means that obtains surface topography and composition information of a sample by scanning the surface of the sample and by detecting secondary or reflected electron signals from the sample.
Further, S2 comprises the following sub-steps:
s21, determining color weights of all pixel points according to RGB components of all pixel points in the feature image;
s22, constructing a state characteristic model, inputting the color weight of each pixel point into the state characteristic model, and determining the state characteristic value of each pixel point;
s23, generating a state feature matrix of the feature image according to the state feature values of the pixel points.
Further, in S21, the color weight q of the pixel point in the ith row and the jth column in the feature image ij The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein R is ij Representing the red component, G, of the ith row and jth column pixel points in the feature image ij Representing green component of ith row and jth column pixel points in characteristic image, B ij Representing blue component of pixel point in ith row and jth column in characteristic image, R 0 Representing the mean value of red components of all pixel points in the characteristic image, G 0 Representing the green component mean value of all pixel points in the characteristic image, B 0 Representing the mean of the blue components of all pixels in the feature image.
Further, the state feature model comprises a depth convolution module, a global feature module and an output module which are sequentially connected.
The beneficial effects of the above-mentioned further scheme are: in the invention, the depth convolution module comprises convolution kernels with the same number as the pixel points of the characteristic image, and each convolution kernel is used for extracting the edge characteristics of each pixel point in the characteristic image. The output module is used for comprehensively outputting the state characteristic values of the pixel points.
According to the method, red, green and blue components of each pixel point in the characteristic image are compared with the average value of the red, green and blue components of all the pixel points, and the color weight of each pixel point is determined; and inputting each pixel point and the color weight thereof into a state feature model, respectively extracting features of each pixel point by utilizing each convolution kernel of a depth convolution layer in the state feature model, calculating the output of each convolution kernel in the depth convolution module and the color weight value corresponding to each convolution kernel processing pixel point by utilizing a global feature module to obtain global features, calculating the RGB values of the global features and the processing pixels corresponding to each output layer by utilizing each output layer of an output module, and determining the state feature value of each pixel point.
Further, the expression of the global feature module is:wherein Q represents the output of the global feature module, C m Representing the output, K, of the mth convolution kernel in the depth convolution module m Represents the size, q, of the mth convolution kernel in the depth convolution module m Representing color weight corresponding to pixel point processed by mth convolution kernel in depth convolution module, B m The step length of the mth convolution kernel in the depth convolution module is represented, M represents the number of convolution kernels of the depth convolution module, sigma represents the standard deviation of all convolution kernel outputs, exp (·) represents an exponential function, and q represents the hyper-parameter of the global feature module.
Further, the expression of the output module is:wherein Z represents the output of the nth output layer in the output module, Q represents the output of the global feature module, R represents the red component of the pixel point corresponding to the nth output layer, G represents the green component of the pixel point corresponding to the nth output layer, B represents the blue component of the pixel point corresponding to the nth output layer, and ln (·) represents a logarithmic function.
Further, in S23, the specific method for generating the state feature matrix is as follows: the method comprises the steps of taking the number of rows of pixel points of a feature image as the number of rows of a state feature matrix, taking the number of columns of pixel points of the feature image as the number of columns of the state feature matrix, taking the state feature value of each row of pixel points as the element of each row of the state feature matrix, and generating the state feature matrix of the feature image.
Further, S3 comprises the following sub-steps:
s31, determining a red component correction coefficient, a green component correction coefficient and a blue component correction coefficient of each pixel point in the characteristic image according to the state characteristic matrix of the characteristic image;
s32, correcting the characteristic image according to the red component correction coefficient, the green component correction coefficient and the blue component correction coefficient of the characteristic image to obtain a standard surface image, and finishing image processing.
And taking the average value of the red component correction coefficient and the original red component value of each pixel point of the characteristic image as the latest red component value, blue component and green component of the corresponding pixel point in the standard surface image.
Further, in S31, the correction coefficient of the red component of the pixel point in the ith row and the jth column in the feature imageThe calculation formula of (2) is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein lambda represents the eigenvalue of the state eigenvalue matrix, R ij Representing the red component of the ith row and jth column pixel points in the feature image,/and (ii)>Representing an upward rounding, P 0 Representing the position of the pixel point corresponding to the maximum value of the red component in the characteristic image, P ij Representing the position of the pixel point in the ith row and the jth column in the characteristic image, wherein dis (·) represents a Euclidean distance function;
in S31, the green component correction coefficient of the ith row and jth column pixel points in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is ij Representing green component, P of ith row and jth column pixel points in characteristic image 1 Representing the position of the pixel point corresponding to the maximum value of the green component;
in S31, the green component correction coefficient of the ith row and jth column pixel points in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein B is ij Representing blue component, P of pixel point in ith row and jth column in characteristic image 2 The position of the maximum value of the blue component corresponding to the pixel point is indicated.
The beneficial effects of the invention are as follows: the method comprises the steps of carrying out denoising treatment on a surface image of a scanned carbon nanotube sample, extracting a state feature matrix representing pixel point features of the whole image, and obtaining the state feature matrix through convolution, feature calculation and the like; the invention also carries out color correction on the image according to the state characteristic matrix, ensures the definition of the carbon nanotube sample image, and further improves the accuracy of observation.
Drawings
Fig. 1 is a flowchart of a method for processing a carbon nanotube preparation state image.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a method for processing a carbon nanotube preparation status image, comprising the steps of:
s1, scanning the surface of a carbon nanotube sample by using a scanning electron microscope to obtain a surface image, and denoising the surface image to obtain a characteristic image;
s2, determining state characteristic values of all pixel points in the characteristic image to obtain a state characteristic matrix of the characteristic image;
s3, correcting the characteristic image according to the state characteristic matrix of the characteristic image to obtain a standard surface image, and finishing image processing.
Scanning electron microscopy is a common characterization means that obtains surface topography and composition information of a sample by scanning the surface of the sample and by detecting secondary or reflected electron signals from the sample.
In an embodiment of the present invention, S2 comprises the following sub-steps:
s21, determining color weights of all pixel points according to RGB components of all pixel points in the feature image;
s22, constructing a state characteristic model, inputting the color weight of each pixel point into the state characteristic model, and determining the state characteristic value of each pixel point;
s23, generating a state feature matrix of the feature image according to the state feature values of the pixel points.
In the embodiment of the invention, in S21, the color weight q of the pixel point in the ith row and the jth column in the feature image ij The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein R is ij Representing the red component, G, of the ith row and jth column pixel points in the feature image ij Representing green component of ith row and jth column pixel points in characteristic image, B ij Representing blue component of pixel point in ith row and jth column in characteristic image, R 0 Representing the mean value of red components of all pixel points in the characteristic image, G 0 Representing the green component mean value of all pixel points in the characteristic image, B 0 Representing the mean of the blue components of all pixels in the feature image.
In the embodiment of the invention, the state characteristic model comprises a depth convolution module, a global characteristic module and an output module which are sequentially connected.
In the invention, the depth convolution module comprises convolution kernels with the same number as the pixel points of the characteristic image, and each convolution kernel is used for extracting the edge characteristics of each pixel point in the characteristic image. The output module is used for comprehensively outputting the state characteristic values of the pixel points.
According to the method, red, green and blue components of each pixel point in the characteristic image are compared with the average value of the red, green and blue components of all the pixel points, and the color weight of each pixel point is determined; and inputting each pixel point and the color weight thereof into a state feature model, respectively extracting features of each pixel point by utilizing each convolution kernel of a depth convolution layer in the state feature model, calculating the output of each convolution kernel in the depth convolution module and the color weight value corresponding to each convolution kernel processing pixel point by utilizing a global feature module to obtain global features, calculating the RGB values of the global features and the processing pixels corresponding to each output layer by utilizing each output layer of an output module, and determining the state feature value of each pixel point.
In the embodiment of the invention, the expression of the global feature module is as follows:wherein Q represents the output of the global feature module, C m Representing the output, K, of the mth convolution kernel in the depth convolution module m Represents the size, q, of the mth convolution kernel in the depth convolution module m Representing color weight corresponding to pixel point processed by mth convolution kernel in depth convolution module, B m The step length of the mth convolution kernel in the depth convolution module is represented, M represents the number of convolution kernels of the depth convolution module, sigma represents the standard deviation of all convolution kernel outputs, exp (·) represents an exponential function, and q represents the hyper-parameter of the global feature module.
In the embodiment of the invention, the expression of the output module is:wherein Z represents the output of the nth output layer in the output module, Q represents the output of the global feature module, R represents the red component of the pixel point corresponding to the nth output layer, G represents the green component of the pixel point corresponding to the nth output layer, B represents the blue component of the pixel point corresponding to the nth output layer, and ln (·) represents a logarithmic function.
In the embodiment of the present invention, in S23, a specific method for generating the state feature matrix is as follows: the method comprises the steps of taking the number of rows of pixel points of a feature image as the number of rows of a state feature matrix, taking the number of columns of pixel points of the feature image as the number of columns of the state feature matrix, taking the state feature value of each row of pixel points as the element of each row of the state feature matrix, and generating the state feature matrix of the feature image.
In an embodiment of the present invention, S3 comprises the following sub-steps:
s31, determining a red component correction coefficient, a green component correction coefficient and a blue component correction coefficient of each pixel point in the characteristic image according to the state characteristic matrix of the characteristic image;
s32, correcting the characteristic image according to the red component correction coefficient, the green component correction coefficient and the blue component correction coefficient of the characteristic image to obtain a standard surface image, and finishing image processing.
And taking the average value of the red component correction coefficient and the original red component value of each pixel point of the characteristic image as the latest red component value, blue component and green component of the corresponding pixel point in the standard surface image.
In the embodiment of the present invention, in S31, the correction coefficient of the red component of the pixel point in the ith row and the jth column in the feature imageThe calculation formula of (2) is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein lambda represents the eigenvalue of the state eigenvalue matrix, R ij Representing the red component of the ith row and jth column pixel points in the feature image,/and (ii)>Representing an upward rounding, P 0 Representing the position of the pixel point corresponding to the maximum value of the red component in the characteristic image, P ij Representing the position of the pixel point in the ith row and the jth column in the characteristic image, wherein dis (·) represents a Euclidean distance function;
in S31, the green component correction coefficient of the ith row and jth column pixel points in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is ij Representing green component, P of ith row and jth column pixel points in characteristic image 1 Representing the position of the pixel point corresponding to the maximum value of the green component;
in S31, the green component of the ith row and jth column pixels in the feature image is correctedCoefficients ofThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein B is ij Representing blue component, P of pixel point in ith row and jth column in characteristic image 2 The position of the maximum value of the blue component corresponding to the pixel point is indicated.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (4)
1. A method for processing a state image of a carbon nanotube preparation, comprising the steps of:
s1, scanning the surface of a carbon nanotube sample by using a scanning electron microscope to obtain a surface image, and denoising the surface image to obtain a characteristic image;
s2, determining state characteristic values of all pixel points in the characteristic image to obtain a state characteristic matrix of the characteristic image;
s3, correcting the characteristic image according to the state characteristic matrix of the characteristic image to obtain a standard surface image, and finishing image processing;
the step S2 comprises the following substeps:
s21, determining color weights of all pixel points according to RGB components of all pixel points in the feature image;
s22, constructing a state characteristic model, inputting the color weight of each pixel point into the state characteristic model, and determining the state characteristic value of each pixel point;
s23, generating a state feature matrix of the feature image according to the state feature values of the pixel points;
the state feature model comprises a depth convolution module, a global feature module and an output module which are sequentially connected;
the expression of the global feature module is as follows:wherein Q represents the output of the global feature module, C m Representing the output, K, of the mth convolution kernel in the depth convolution module m Represents the size, q, of the mth convolution kernel in the depth convolution module m Representing color weight corresponding to pixel point processed by mth convolution kernel in depth convolution module, B m The step length of the mth convolution kernel in the depth convolution module is represented, M represents the number of convolution kernels of the depth convolution module, sigma represents the standard deviation of all convolution kernel outputs, exp (·) represents an exponential function, and q represents the super-parameter of the global feature module;
the expression of the output module is as follows:wherein Z represents the output of the nth output layer in the output module, Q represents the output of the global feature module, R represents the red component of the pixel point corresponding to the nth output layer, G represents the green component of the pixel point corresponding to the nth output layer, B represents the blue component of the pixel point corresponding to the nth output layer, and ln (·) represents a logarithmic function;
in S23, the specific method for generating the state feature matrix is as follows: the method comprises the steps of taking the number of rows of pixel points of a feature image as the number of rows of a state feature matrix, taking the number of columns of pixel points of the feature image as the number of columns of the state feature matrix, taking the state feature value of each row of pixel points as the element of each row of the state feature matrix, and generating the state feature matrix of the feature image.
2. The method for processing a carbon nanotube preparation state image according to claim 1, wherein in S21, a color weight q of a pixel point in an ith row and a jth column in a feature image ij The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein R is ij Representing the red component, G, of the ith row and jth column pixel points in the feature image ij Representing green component of ith row and jth column pixel points in characteristic image, B ij Representing blue component of pixel point in ith row and jth column in characteristic image, R 0 Representing the mean value of red components of all pixel points in the characteristic image, G 0 Representing the green component mean value of all pixel points in the characteristic image, B 0 Representing the mean of the blue components of all pixels in the feature image.
3. The method for processing a carbon nanotube preparation state image according to claim 1, wherein S3 comprises the sub-steps of:
s31, determining a red component correction coefficient, a green component correction coefficient and a blue component correction coefficient of each pixel point in the characteristic image according to the state characteristic matrix of the characteristic image;
s32, correcting the characteristic image according to the red component correction coefficient, the green component correction coefficient and the blue component correction coefficient of the characteristic image to obtain a standard surface image, and finishing image processing.
4. The method according to claim 3, wherein in S31, the correction coefficient of the red component of the ith row and jth column pixels in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein lambda represents the eigenvalue of the state eigenvalue matrix, R ij Representing the red component of the ith row and jth column pixel points in the feature image,/and (ii)>Representing an upward rounding, P 0 Representing maximum corresponding image of red component in characteristic imageThe position of the prime point, P ij Representing the position of the pixel point in the ith row and the jth column in the characteristic image, wherein dis (·) represents a Euclidean distance function;
in S31, the green component correction coefficient of the ith row and jth column pixel points in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is ij Representing green component, P of ith row and jth column pixel points in characteristic image 1 Representing the position of the pixel point corresponding to the maximum value of the green component;
in S31, the green component correction coefficient of the ith row and jth column pixel points in the feature imageThe calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein B is ij Representing blue component, P of pixel point in ith row and jth column in characteristic image 2 The position of the maximum value of the blue component corresponding to the pixel point is indicated.
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