CN113256587A - Ionized layer plasma bubble airglow image automatic processing method and device - Google Patents
Ionized layer plasma bubble airglow image automatic processing method and device Download PDFInfo
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Abstract
The invention discloses an automatic ionospheric plasma bubble airglow image processing method and device, which comprises the following steps: acquiring optical observation data of the all-sky airglow imager, and providing data support for airglow image batch processing; and extracting the coordinate position of the zenith according to the parameter information in the original image of the ionized layer plasma bubble, and calculating the rotation angle required by azimuth correction. And (3) reading a plurality of original airglow images in batch, constructing a self-adaptive image processing algorithm model, and obtaining a plasma bubble image product with high clear resolution. According to the characteristics of different brightness differences of original airglow images, by adopting an intelligent identification method of average gray values of the images and utilizing a self-adaptive image brightness enhancement technology, the airglow original observation data are processed in batches, so that the aim of automatically processing the airglow images is fulfilled. The invention adopts a airglow image batch automatic image processing method, and solves the problem that a large number of airglow images need manual processing and have low efficiency.
Description
Technical Field
The invention relates to the technical field of ionosphere optical imaging, in particular to an ionosphere plasma bubble airglow image automatic processing method and device.
Background
The optical observation means is an effective means for observing the ionized layer plasma bubble in a large range and at low cost, the plasma irregular structure (also called plasma bubble) in the ionized layer F region is the main reason of low-latitude ionized layer flicker, the research on the ionized layer plasma bubble has strong scientific significance and application value, the information such as plasma density and motion related to the emission intensity of a plasma is obtained by mainly carrying out imaging observation on the airglow with a certain specific wavelength on the height of the ionized layer, and various information such as the position, the structure, the size, the motion speed and the like of the ionized layer plasma bubble can be reflected. The all-sky airglow imager extracts the airglow radiation intensity information of the middle-high atmospheric characteristic height area by using the narrow-band filter, and records the all-sky airglow radiation intensity distribution by using the high-sensitivity CCD; the digital image capture device has a wide field of view, can continuously take multiband pictures, forms a digital two-dimensional airglow image and can obtain airglow information in a range of 180 degrees in the whole sky. The brightness of a night airglow image obtained by observing by an airglow imager is low, the overall gray value of the image is small, the influence of background radiation on airglow is avoided, the brightness of the image is uneven, and the noise of bright spots of stars exists, so that the information of plasma bubbles cannot be directly observed from an original image.
In order to facilitate the analysis and research of the plasma bubble image, an image processing method is required to perform image processing on the acquired original airglow image. The original observation image is preprocessed by adopting a series of digital image processing technical methods such as image enhancement, orientation correction, median filtering and the like so as to obtain an ionized layer plasma bubble image product which can be clearly identified. However, the image preprocessing of the plasma bubble usually adopts a manual processing method, and the original image data is more, so that the airglow image processing efficiency is low.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method and a device for automatically processing an ionized layer plasma bubble airglow image, which can efficiently and automatically process the plasma bubble image.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a method and an apparatus for automatically processing an ionospheric plasma bubble airglow image, wherein the method comprises the following steps:
acquiring an original airglow image of the plasma bubble;
reading the original airglow images of the plasma bubbles in batches to obtain corresponding two-dimensional airglow gray images and the number of the airglow images of the plasma bubbles;
carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image;
carrying out circular template extraction on the enhanced plasma bubble image, and carrying out azimuth correction to obtain an ionosphere plasma bubble image product;
and outputting the ionized layer plasma bubble image products to a specified folder in batches.
Further, the method for obtaining the enhanced plasma bubble image by utilizing the image parameter intelligent identification technology to perform self-adaptive brightness improvement on the two-dimensional airglow gray level image comprises the following steps:
inputting the two-dimensional airglow gray image into a self-adaptive image processing algorithm model, and obtaining the average gray value ave (n) of each airglow image through an intelligent airglow image average gray value discrimination technology;
and performing differential image processing on the two-dimensional airglow gray images with obviously different brightness differences by adopting a self-adaptive image brightness enhancement algorithm to obtain enhanced plasma bubble images with uniform and recognizable brightness distribution.
Further, the calculation formula of the average gray value ave (n) of the airglow image is as follows:
wherein Ave (n) is the average gray value of the airglow image, AijIs the gray value, N, of each pixel point of the airglow image2The number of the pixel points of the airglow image.
Further, the threshold of the average gray value ave (n) of the two-dimensional airglow gray image is set to be 128;
the average gray value of the original airglow image of the plasma bubble is between 0 and 255;
the gray scale values of the enhanced plasma bubble image are uniformly distributed between 0 and 255, and the average gray scale value is about 128.
Further, the method for extracting the circular template of the enhanced plasma bubble image and performing orientation correction comprises the following steps:
calculating the position coordinates (X) of the zenith based on the original airglow image of the plasma bubble0，Y0) And the angle theta of rotation required for azimuth correction;
coordinate (X) of the position of the zenith0，Y0) As the position of the circle center of the circular template, extracting a circular plasma bubble image of the airglow image through the circular template;
and rotating the round plasma bubble image of the airglow image counterclockwise by the rotation angle theta required by azimuth correction by adopting a nearest neighbor interpolation method to obtain an ionosphere plasma bubble image product after azimuth correction.
Further, the circular plasma bubble image of the airglow image is composed of
Grid points, and the radius of the extracted circular template is 451.
Further, the method for correcting the counterclockwise rotation azimuth of the circular plasma bubble image of the airglow image by the required rotation angle theta by adopting the nearest neighbor interpolation method comprises the following steps: the image is rotated as follows:
X/x＝W/w
Y/y＝H/h
f(X,Y)＝f(W/w*x,H/h*y)
wherein, W and H are pixel sizes of the original image, W and H are pixel sizes of the zoomed image, (X, Y) are pixel points of the original image, (X, Y) are pixel points of the zoomed image, and f (X, Y) is gray value of the pixel point corresponding to the airglow image.
Further, the position coordinates (X) of the zenith are calculated0，Y0) The method comprises the following steps: the calculation is solved with the following formula:
θ1＝Azim2-Azim1
θ2＝Azim3-Azim1
wherein (X)1，Y1)，(X2，Y2)，(X3，Y3) For the coordinates of three uniformly distributed star points in the selected airglow image, (X)0，Y0) Extracting the circle center position of the circular template, namely the zenith position; theta1andθ2The angle between two triangles formed by connecting three star points, Azim1，Azim2，Azim3The corresponding azimuth angles of three stars in the starry sky plot.
Further, the method for calculating the angle θ of rotation required for azimuth correction includes: the calculation is solved with the following formula:
wherein (X)0，Y0) As the center of a circle, i.e. the position of the zenith, (X)n，Yn) Is the pixel position of the North Star, θNThe azimuth of the north star.
In a second aspect, the present invention provides an apparatus for automatically processing an ionospheric plasma bubble airglow image, the apparatus comprising:
an image acquisition module: the method comprises the steps of obtaining an original airglow image of a plasma bubble;
the module is read in batches: the plasma bubble brightness detection device is used for reading the original plasma bubble brightness images in batches to obtain corresponding two-dimensional brightness gray images and the quantity of the plasma bubble brightness images;
an image enhancement module: the two-dimensional airglow gray level image processing device is used for carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image;
an extraction and correction module: the device is used for performing circular template extraction on the enhanced plasma bubble image and performing azimuth correction to obtain an ionosphere plasma bubble image product;
a batch output module: and the ionized layer plasma bubble image production system is used for outputting the ionized layer plasma bubble image products to a specified folder in batches.
Compared with the prior art, the invention has the following beneficial effects:
1. different from a common plasma bubble image preprocessing method, the method adopts a self-adaptive image brightness improvement technology, solves the problems that a large number of airglow images need to be manually processed and the steps are complicated, and effectively improves the airglow image processing efficiency;
2. the method breaks through the traditional thought of manually processing the plasma brightness images, adopts an intelligent identification method of the average gray value of the images and utilizes the self-adaptive image brightness enhancement technology to realize the batch automatic processing of the plasma bubble brightness images of the ionized layer, and can efficiently and automatically process the plasma bubble images;
3. the invention provides advanced optical imaging technology basis and technical support with high space-time resolution for the follow-up low latitude ionospheric scintillation real-time monitoring and the nowcast.
Drawings
FIG. 1 is a flow chart of automated airglow image processing according to the present invention.
FIG. 2 is a diagram of raw airglow image data acquired in the present invention.
Fig. 3 is an enhanced airglow image after automated processing according to the present invention.
FIG. 4 is a comparison graph of gray scale distribution test results of an original airglow image and an automatically processed image according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
the embodiment provides an automatic ionospheric plasma bubble airglow image processing method, which comprises the following steps: acquiring an original airglow image of the plasma bubble; reading the original airglow images of the plasma bubbles in batches to obtain corresponding two-dimensional airglow gray images and the number of the airglow images of the plasma bubbles; carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image; carrying out circular template extraction on the enhanced plasma bubble image, and carrying out azimuth correction to obtain an ionosphere plasma bubble image product; and outputting the ionized layer plasma bubble image products to a specified folder in batches.
The ionized layer plasma bubble airglow image batch automatic processing method of the embodiment performs batch automatic image processing on any number of original airglow images to obtain clearly identifiable plasma bubble image products. Therefore, the method comprises the steps of firstly reading original airglow observation data of the plasma bubbles in batches, secondly adopting self-adaptive brightness improvement on an original airglow image by utilizing an image parameter intelligent identification technology, carrying out circular template extraction on the enhanced image, carrying out azimuth correction, and finally outputting the processed airglow image to a designated folder in batches. The method provides advanced optical imaging technical support with high space-time resolution for low latitude ionospheric scintillation real-time monitoring and nowcast.
As shown in fig. 1, the method of this embodiment specifically includes the following steps:
step 1: and downloading optical observation data of the ionized layer plasma bubble from a meridian engineering data center website.
The brightness of one of the obtained plasma bubble original airglow images is improved by 4.5 times; selecting three uniformly distributed star points according to the enhanced image, wherein the coordinates are respectively (X)1，Y1)，(X2，Y2)，(X3，Y3) (ii) a Calculating the position coordinate (X) of zenith according to the cosine theorem0，Y0) And positioning to the Polaris through star map software stellarium, acquiring the pixel position of the Polaris, and further calculating the rotation angle theta required by azimuth correction.
Step 2: reading the original airglow images obtained in the step 1 in batches to obtain two-dimensional airglow gray images in a png format in a corresponding quantity; the airglow images are composed of 1024 x 1024 grid points, and the gray value range of the airglow images is 0-255; the number of plasma bubble airglow images read by Matlab software was N.
And step 3: inputting the airglow data obtained in the step (2) into a self-adaptive image processing algorithm model, and intelligently judging the average gray value of the airglow image to obtain the average gray value Ave (n) of each image; and performing differential image processing on a plurality of airglow images with different brightness differences by adopting a self-adaptive image brightness enhancement algorithm, thereby obtaining plasma bubble images with uniform brightness distribution and capable of being identified.
And 4, step 4: further image processing is carried out on the plasma bubble image after brightness enhancement, circular template extraction and azimuth correction are carried out on the plasma bubble image, and the zenith position (X) calculated in the step 1 is utilized0，Y0) Extracting the position of the circle center of the circular template and extracting a circular plasma bubble image of the airglow image; rotating the image by theta anticlockwise by taking the zenith position as a center, and rotating the image by adopting a nearest linear interpolation method to obtain an accurate corrected image and finally obtain a clear ionospheric plasma bubble image product after azimuth correction; and outputting and storing the processed plasma bubble images in a designated folder in batches.
In this embodiment, the step 1 is a specific method for determining the position coordinates (X) of the zenith0，Y0) And the angle θ of the azimuth correction rotation, the main formula is:
wherein (X)1，Y1)，(X2，Y2)，(X3，Y3) For the coordinates of three uniformly distributed star points in the selected airglow image, (X)0，Y0) The circle center position of the circular template, namely the zenith position, is extracted. Theta1andθ2The angle between two triangles formed by connecting three star points, Azim1，Azim2，Azim3The azimuth angle corresponding to three stars in stellarium software starry sky plot.
In order to calculate the angle theta of the airglow image required to be rotated for azimuth correction, the specific formula is as follows:
wherein (X)0，Y0) As the center of a circle, i.e. the position of the zenith, (X)n，Yn) Picture pixel position, θ, of PolarisNThe azimuth of the north star.
In this embodiment, in the step 3, the average gray value ave (n) of each image is obtained by intelligently identifying the average gray value ave (n) of the two-dimensional airglow image, and the specific formula is as follows:
wherein Ave (n) is the average gray value of the airglow image, AijIs the gray value, N, of each pixel point of the airglow image2The number of the pixel points of the airglow image.
In this embodiment, in the step 4, the image is rotated by using the nearest linear interpolation method, and the specific formula is as follows:
X/x＝W/w (4)
Y/y＝H/h (5)
f(X,Y)＝f(W/w*x,H/h*y) (6)
wherein, W and H are pixel sizes of the original image, W and H are pixel sizes of the zoomed image, (X, Y) are pixel points of the original image, (X, Y) are pixel points of the zoomed image, and f (X, Y) is gray value of the pixel point corresponding to the airglow image.
Based on the above, in order to obtain a large amount of airglow image data, original airglow image data of airglow plasma bubbles from 9/1/9/30/2014 is acquired from a meridian engineering data center website, an original airglow image composed of 1024X 1024 grid points is formed, required parameter information is extracted from the acquired original airglow image of the plasma bubbles, and the position coordinates (X) of the zenith are calculated0，Y0) And the angle theta of rotation required for azimuth correction. The original airglow image data obtained in the present invention is shown in fig. 2, fig. 3 is an enhanced airglow image after the automatic processing of the present invention, fig. 4 is a comparison graph of the gray distribution test results of the original airglow image and the automatic processing image, and an example is airglow data at 16:57:07UT time in 9/23/2014. In fig. 4, the left images (a) are graphs of the gray distribution inspection result of the original airglow image, and the right images (b) are graphs of the gray distribution inspection result of the automatically processed image.
And D, reading the airglow images obtained in the step one in batches to obtain two-dimensional airglow gray images with corresponding quantity, and obtaining the quantity N of the airglow images of the plasma bubbles. Inputting the airglow data processed in the step two into a self-adaptive image processing algorithm model, and obtaining an average gray value ave (n) of each image through an intelligent judgment technology of the average gray value of the airglow images; and a self-adaptive image brightness enhancement algorithm is adopted to perform differentiated image processing on the airglow images with obviously different brightness differences to obtain plasma bubble images with uniform brightness distribution and capability of being recognized. Further image processing is carried out on the plasma bubble image after brightness enhancement, circular template extraction and azimuth correction are carried out on the plasma bubble image, and the zenith position (X) obtained in the step one is utilized0，Y0) I.e. extracting the circular template circleExtracting a circular plasma bubble image of the airglow image at the position of the heart; further rotating the image by theta anticlockwise to finally obtain a clear ionosphere plasma bubble image product after azimuth correction; and outputting and storing the processed plasma bubble images in a designated folder in batches.
Example two:
the embodiment provides an automatic ionospheric plasma bubble airglow image processing apparatus, comprising:
an image acquisition module: the method comprises the steps of obtaining an original airglow image of a plasma bubble;
the module is read in batches: the plasma bubble brightness detection device is used for reading the original plasma bubble brightness images in batches to obtain corresponding two-dimensional brightness gray images and the quantity of the plasma bubble brightness images;
an image enhancement module: the two-dimensional airglow gray level image processing device is used for carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image;
an extraction and correction module: the device is used for performing circular template extraction on the enhanced plasma bubble image and performing azimuth correction to obtain an ionosphere plasma bubble image product;
a batch output module: and the ionized layer plasma bubble image production system is used for outputting the ionized layer plasma bubble image products to a specified folder in batches.
The device can realize the following method:
step 1: optical observation data of the ionized layer plasma bubble are downloaded from a meridian engineering data center website,
the brightness of one of the obtained plasma bubble original airglow images is improved by 4.5 times; selecting three uniformly distributed star points according to the enhanced image, wherein the coordinates are respectively (X)1，Y1)，(X2，Y2)，(X3，Y3) (ii) a Calculating the position coordinate (X) of zenith according to the cosine theorem0，Y0) Positioning the north polar star by star map software stellerium, acquiring the pixel position of the north polar star, and calculating the rotation required by azimuth correctionThe angle theta.
Step 2: reading the original airglow images obtained in the step 1 in batches to obtain two-dimensional airglow gray images in a png format in a corresponding quantity; the airglow images are composed of 1024 x 1024 grid points, and the gray value range of the airglow images is 0-255; the number of plasma bubble airglow images read by Matlab software was N.
And step 3: inputting the airglow data obtained in the step (2) into a self-adaptive image processing algorithm model, and intelligently judging the average gray value of the airglow image to obtain the average gray value Ave (n) of each image; and performing differential image processing on a plurality of airglow images with different brightness differences by adopting a self-adaptive image brightness enhancement algorithm, thereby obtaining plasma bubble images with uniform brightness distribution and capable of being identified.
And 4, step 4: further image processing is carried out on the plasma bubble image after brightness enhancement, circular template extraction and azimuth correction are carried out on the plasma bubble image, and the zenith position (X) calculated in the step 1 is utilized0，Y0) Extracting the position of the circle center of the circular template and extracting a circular plasma bubble image of the airglow image; rotating the image by theta anticlockwise by taking the zenith position as a center, and rotating the image by adopting a nearest linear interpolation method to obtain an accurate corrected image and finally obtain a clear ionospheric plasma bubble image product after azimuth correction; and outputting and storing the processed plasma bubble images in a designated folder in batches.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An automatic ionospheric plasma bubble airglow image processing method, comprising the steps of:
acquiring an original airglow image of the plasma bubble;
reading the original airglow images of the plasma bubbles in batches to obtain corresponding two-dimensional airglow gray images and the number of the airglow images of the plasma bubbles;
carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image;
carrying out circular template extraction on the enhanced plasma bubble image, and carrying out azimuth correction to obtain an ionosphere plasma bubble image product;
and outputting the ionized layer plasma bubble image products to a specified folder in batches.
2. The method for automatically processing the ionospheric plasma bubble airglow image according to claim 1, wherein an image parameter intelligent recognition technology is used to adaptively improve the brightness of the two-dimensional airglow gray-scale image, and the method for obtaining the enhanced plasma bubble image comprises the following steps:
inputting the two-dimensional airglow gray image into a self-adaptive image processing algorithm model, and obtaining the average gray value ave (n) of each airglow image through an intelligent airglow image average gray value discrimination technology;
and performing differential image processing on the two-dimensional airglow gray images with obviously different brightness differences by adopting a self-adaptive image brightness enhancement algorithm to obtain enhanced plasma bubble images with uniform and recognizable brightness distribution.
3. The method for automatically processing the airglow image of the ionized layer plasma bubble according to claim 2, wherein the calculation formula of the average gray value ave (n) of the airglow image is as follows:
wherein Ave (n) is the average gray value of the airglow image, AijIs the gray value, N, of each pixel point of the airglow image2The number of the pixel points of the airglow image.
4. The automated ionospheric plasma bubble airglow image processing method according to claim 2, wherein a threshold value of the average gray value ave (n) of the two-dimensional airglow gray image is set to 128;
the average gray value of the original airglow image of the plasma bubble is between 0 and 255;
the gray scale value of the enhanced plasma bubble image is uniformly distributed between 0 and 255, and the average gray scale value is about 128.
5. The method for automatically processing the ionospheric plasma bubble airglow images of claim 1, wherein the method for performing circular template extraction and orientation correction on the enhanced plasma bubble images comprises the following steps:
calculating the position coordinates (X) of the zenith based on the original airglow image of the plasma bubble0，Y0) And the angle theta of rotation required for azimuth correction;
coordinate (X) of the position of the zenith0，Y0) As the position of the circle center of the circular template, extracting a circular plasma bubble image of the airglow image through the circular template;
and rotating the round plasma bubble image of the airglow image counterclockwise by the rotation angle theta required by azimuth correction by adopting a nearest neighbor interpolation method to obtain an ionosphere plasma bubble image product after azimuth correction.
6. The automated ionospheric plasma bubble airglow image processing method according to claim 5, wherein the circular plasma bubble image of the airglow image is composed of 903 x 903 grid points, and the radius of the extracted circular template is 451.
7. The automated ionospheric plasma bubble airglow image processing method of claim 5, wherein the method for correcting the orientation of the counterclockwise rotation of the circular plasma bubble image of the airglow image by the required rotation angle θ using nearest neighbor interpolation comprises: the image is rotated as follows:
X/x＝W/w
Y/y＝H/h
f(X,Y)＝f(W/w*x,H/h*y)
wherein, W and H are pixel sizes of the original image, W and H are pixel sizes of the zoomed image, (X, Y) are pixel points of the original image, (X, Y) are pixel points of the zoomed image, and f (X, Y) is gray value of the pixel point corresponding to the airglow image.
8. The method of claim 5, wherein the zenith position coordinates (X) are calculated0，Y0) The method comprises the following steps: the calculation is solved with the following formula:
θ1＝Azim2-Azim1
θ2＝Azim3-Azim1
wherein (X)1，Y1)，(X2，Y2)，(X3，Y3) For the coordinates of three uniformly distributed star points in the selected airglow image, (X)0，Y0) Extracting the circle center position of the circular template, namely the zenith position; theta1andθ2The angle between two triangles formed by connecting three star points, Azim1，Azim2，Azim3The corresponding azimuth angles of three stars in the starry sky plot.
9. The method of automated ionospheric plasma bubble airglow image processing according to claim 5, wherein the method of calculating the angle θ of rotation required for azimuth correction comprises: the calculation is solved with the following formula:
wherein, (X0, Y0) is the position of the centre of a circle, namely the zenith, (Xn, Yn) is the pixel position of the Polaris, thetaNThe azimuth of the north star.
10. An apparatus for automatically processing ionospheric plasma bubble airglow images, the apparatus comprising:
an image acquisition module: the method comprises the steps of obtaining an original airglow image of a plasma bubble;
the module is read in batches: the plasma bubble brightness detection device is used for reading the original plasma bubble brightness images in batches to obtain corresponding two-dimensional brightness gray images and the quantity of the plasma bubble brightness images;
an image enhancement module: the two-dimensional airglow gray level image processing device is used for carrying out self-adaptive brightness improvement on the two-dimensional airglow gray level image by utilizing an image parameter intelligent identification technology to obtain an enhanced plasma bubble image;
an extraction and correction module: the device is used for performing circular template extraction on the enhanced plasma bubble image and performing azimuth correction to obtain an ionosphere plasma bubble image product;
a batch output module: and the ionized layer plasma bubble image production system is used for outputting the ionized layer plasma bubble image products to a specified folder in batches.
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