CN107369175A - A kind of oyster for aquaculture arranges Area computing method and system - Google Patents
A kind of oyster for aquaculture arranges Area computing method and system Download PDFInfo
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Abstract
The application is related to a kind of oyster for aquaculture and arranges Area computing method and system, the measuring method obtains view data corresponding to oyster row's area by way of multispectral remote sensing, described image data are carried out with geometric correction, correction, then image enhancement processing is carried out, with the prominent useful information related from oyster row's area and expand the different direct characteristic differences of atural object, the image that grid is obtained to processing carries out vector quantization, chooses the related feature of oyster row's area using the spatial database being connected to calculate oyster row's area.By such a mode, the application is handled the view data of remote sensing images, it is possible to achieve automatic interpretation and calculating, is reduced cost of labor, and can be improved computational accuracy by geometric correction and correction.
Description
Technical Field
The application relates to the technical field of measurement and calculation, in particular to an oyster arrangement area measurement and calculation method and system for aquaculture.
Background
Oyster, named oyster by its scholar name, belongs to the phylum mollusca, the family of oysters. Is the most common bivalve shellfish along the sea and has various varieties. More than 200 species of oysters exist in the world, and China has species of crassostrea gigas, ostrea rivularis and the like. The offshore oyster cultivation method mainly comprises a stone shell attachment cultivation method, a pile cultivation method, a bridge cultivation method, a felling cultivation method, a pile-connected cultivation method and the like. Along with the development of oyster cultivation cardinality and the improvement of social environment protection consciousness, the fell cultivation method is more and more popular. The raft culture method is one of oyster three-dimensional culture methods, is of a multi-purpose longline type, and the cultured oysters grow fast and have high yield per unit area. The most of the cultivated oysters are the seeds collected by a shell seedling collector, the shells are connected in series by ropes, the length of the shells is determined according to the depth of water, and the shells are hung under a raft.
The oyster cultivation area in China is increased from 112818 hectares in 2009 to 141498 hectares in 2015, and the oyster industry yield is increased from 350.38 million tons in 2009 to 457.34 million tons in 2015. Because the oyster cultivation scale is continuously enlarged, the offshore excessive cultivation and the cultivation environment are deteriorated, the coastal oyster cultivation area industry needs to be intensively monitored and managed, and how to detect that the coastal oyster cultivation area in each area is more important. The traditional monitoring means can not meet the real requirement, and has great limitation and risk, so that a new monitoring means and a new monitoring method are required.
Remote sensing has the advantages of wide detection range, high data acquisition speed, short period, strong timeliness, low cost, great economic benefit and the like. The remote sensing image can be used for quickly extracting the area of the needed aquaculture, helping the farm to select sites, determining breeding varieties and monitoring the breeding density and the pollution (red tide, water quality and the like) of the breeding water body; by combining with GIS (Geographic Information system) technology, the method can also plan and manage the culture area and evaluate the influence of the aquaculture area on the environment.
There is no method for identifying the most common and optimal aquaculture area, limited by objective factors such as study time, study area and data source. The current commonly used aquaculture area identification methods mainly include visual interpretation, information extraction based on ratio index analysis, information extraction based on neighborhood analysis, information extraction based on corresponding analysis, and the like.
The visual interpretation is to combine with various non-remote sensing information data according to the visual interpretation mark and interpretation experience of the remote sensing image, and to use the relevant knowledge to carry out comprehensive analysis and logical reasoning, so as to obtain the required thematic information from the remote sensing image.
The ratio index is in the multispectral wave band of the same image, the ratio of the brightness value of each pixel in different wave bands is obtained, a new image is formed, the influence of certain factors or backgrounds causing illumination difference is reduced, and the radiation characteristic of a target ground object is highlighted.
The corresponding analysis is an analysis method developed on the basis of factor analysis, also called R-Q type factor analysis.
Neighborhood analysis is a method for spatially analyzing each pixel of a wave band according to the neighboring pixels around, and the number and position of the analyzed and operated pixels are determined by a scanning window.
However, oyster cultivation land has very similar spectral characteristics with other water body types, and conventional multispectral remote sensing can only provide discontinuous spectral band information with spectral resolution of more than 100nm (nanometers). In the oyster cultivation area identification method, the problems of foreign matter co-spectrum cannot be well solved by using visual interpretation, ratio index analysis and corresponding analysis methods, and pepper salt noise can be generated in classification results.
Therefore, there is a need in the art for a new technique to solve the above-mentioned problems.
Disclosure of Invention
Therefore, it is necessary to provide an oyster rank area calculation method and system for aquaculture, which can reduce labor cost and improve calculation accuracy.
An oyster row area measuring and calculating method for aquaculture comprises the following steps:
acquiring image data corresponding to the oyster arrangement area in a multispectral remote sensing mode;
performing geometric correction on the image data, and correcting the deformed image data;
carrying out image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects;
and processing the image data subjected to image enhancement processing to obtain a grid-form image, carrying out vectorization on the image, and selecting features related to the oyster arrangement area by using a connected spatial database to calculate the oyster arrangement area.
An oyster placement area measurement and calculation system for aquaculture, the measurement and calculation system comprising a processor, the processor when executing program instructions:
the processor is used for acquiring image data corresponding to the oyster arrangement area in a multispectral remote sensing mode;
the processor is used for carrying out geometric correction on the image data and rectifying the deviation of the deformed image data;
the processor is used for carrying out image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects;
the processor is used for processing the image data subjected to image enhancement processing to obtain a grid-form image, vectorizing the image, and selecting features related to the oyster arrangement area by using a connected spatial database to calculate the oyster arrangement area.
According to the oyster rank area measuring and calculating method and system for aquaculture, image data corresponding to the oyster rank area is obtained through a multispectral remote sensing mode, geometric correction and deviation rectification are carried out on the image data, image enhancement processing is carried out, useful information related to the oyster rank area is highlighted, direct characteristic differences of different objects are enlarged, vectorization is carried out on the processed image in a grid mode, and features related to the oyster rank area are selected through a connected spatial database to measure and calculate the oyster rank area. By the method, the image data of the remote sensing image is processed, automatic interpretation and calculation can be achieved, labor cost is reduced, and calculation accuracy can be improved through geometric correction and deviation rectification.
Drawings
FIG. 1 is a flowchart illustrating an oyster rank area estimation method for aquaculture according to an embodiment;
FIG. 2 is a diagram illustrating a gray scale value weighted interpolation of pixels during resampling according to an embodiment;
FIG. 3 is a schematic diagram of a coordinate system used in the linear stretch calculation of the present application;
fig. 4 is a block diagram illustrating an oyster rank area estimation system for aquaculture according to an embodiment of the present disclosure;
FIG. 5 is a general flow chart of the oyster rank area calculation system for aquaculture according to the present application
Detailed Description
In an embodiment, referring to fig. 1, the oyster rank area calculation method for aquaculture of this embodiment includes, but is not limited to, the following steps.
And S101, acquiring image data corresponding to the oyster arrangement area in a multispectral remote sensing mode.
In this embodiment S101, multispectral resolution remote sensing may be adopted, and two or more sensors of spectrum channels are used to perform synchronous imaging on the surface feature including the oyster row, so as to divide electromagnetic wave information of radiation reflected by the surface feature into a plurality of spectrum segments for receiving and recording.
S102, performing geometric correction on the image data, and correcting the deformed image data.
In this embodiment S102, the performing geometric correction on the image data may include: and performing geometric correction by selecting ground control points and image resampling.
It should be noted that the geometric correction by selecting the ground control point and resampling the image may include: taking the vectorized topographic map as a base map, carrying out comparative analysis on a remote sensing image of the image data acquired by multispectral remote sensing, and selecting an obvious and clear positioning marker on the base map and the remote sensing image as a ground control point; and resampling the remote sensing image according to the position of each pixel on the output image in the input image, and establishing a new remote sensing image.
It should be noted that, the remote sensing image is resampled according to the position of each pixel on the output image in the input image to create a new remote sensing image, and specifically, a cubic interpolation method may be adopted, and a cubic polynomial is used to solve a manner of approximating a theoretically optimal interpolation function.
Specifically, with a cubic polynomial s (x), an optimal interpolation function sin (x)/x, and the cubic interpolation method of the present embodiment, the expressions include, but are not limited to, the following:
it can be seen that the gray value of the pixel (x, y) to be determined is obtained by weighted interpolation of 16 gray values around the pixel, as shown in fig. 2.
Then the gray scale of the pixel to be solved is calculated as follows:
f(x,y)＝f(i+u,j+v)＝ABC
wherein:
s103, carrying out image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects.
In S103, it should be noted that there are many noises in the image data of the remote sensing image, and these noises not only limit the radiation resolution of the remote sensing image and affect the resolving power for different signal intensities, but also reduce the recognition capability for ground targets and structures, so in this embodiment, the image data after being corrected and rectified is subjected to image enhancement, and specifically, a Frost filter may be used to perform noise reduction on the image data.
It should be noted that the impulse response of the Frost filter employed in this embodiment is a bilateral exponential function, which is approximated to a low-pass filter, the filter parameters of which are determined by the image local variance coefficient, and the attenuation speed of the impulse response depends on the size of the local variance coefficient and is in direct proportion thereto. The front filter is a circular symmetric filter with a weight M value as an adaptive adjustment parameter, and the mathematical expression of the front filter in this embodiment specifically includes the following:
wherein,
in the above mathematical expression of this embodiment, g'(i,j)The pixel gray value is the pixel gray value after smoothing treatment; g(i,j)The original gray value of each pixel in the smooth window;the average value of the pixel gray levels in the window is obtained; m(i,j)The weight index of each corresponding pixel in the smoothing window; t is(i,j)The absolute distance from the central pixel to the adjacent pixels in the smoothing window is calculated; sigma(i,j)Is the variance of the pixel values in the smoothing window;is the size of the smoothing window; l is the imaging view.
Furthermore, after performing noise reduction processing on image data by using a Frost filter, the embodiment may further include: and the dynamic range of the gray value of the remote sensing image is stretched to a specified range according to a linear relation formula in a linear stretching mode, so that the contrast of the remote sensing image is increased. In the embodiment, original pixels with different brightness values in the image are converted into the same brightness value through linear stretching, and the original similar brightness values become dissimilar, so that the contrast of the whole image is enhanced, and the brightness of the image is improved.
It should be noted that, as shown in fig. 3, in the schematic diagram of the coordinate system shown in fig. 3, each grid represents a coordinate point, such as an intermediate pixel unit, whose coordinate is (i, j), the transformation formula of the linear stretching includes:
wherein, G'(i,j)Is gray value with pixel coordinate of (i, j) after linear stretching treatment, G(i,j)Is the gray value with the coordinate of (i, j) of the pixel after the noise reduction processing, C0Is a constant value of C0May be determined from the overall actual image.
And S104, processing the image data subjected to image enhancement to obtain a grid-form image, vectorizing the image, and selecting features related to the oyster arrangement area by using a connected spatial database to calculate the oyster arrangement area.
In this embodiment S104, a professional software may be specifically used to perform vectorization on the processed image in the grid form, and the scope understood by those skilled in the art is not limited thereto.
According to the embodiment of the oyster row area measuring and calculating method for aquaculture, automatic interpretation and calculation can be achieved by processing the image data of the remote sensing image, labor cost is reduced, and calculation accuracy can be improved through geometric correction and deviation correction.
In an embodiment, referring to fig. 3, the oyster rank area measuring and calculating system for aquaculture of the present embodiment includes a processor 30, and when the processor 30 executes a program instruction, the following processes may be implemented, but not limited to. The measuring and calculating system of the present embodiment may further include a memory, which may be an external memory connected to a network, or a memory installed in a wired connection, and is not limited herein.
Specifically, the processor 30 is configured to obtain image data corresponding to the oyster arrangement area in a multispectral remote sensing manner;
the processor 30 is configured to perform geometric correction on the image data and perform deviation rectification on the deformed image data;
the processor 30 is used for performing image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects;
the processor 30 is configured to process the image data subjected to the image enhancement processing to obtain an image in a grid form, perform vectorization on the image, and select features related to the oyster allocation area by using a connected spatial database to calculate the oyster allocation area.
In a specific implementation process, the processor 30 is specifically configured to perform comparative analysis on a remote sensing image of the image data obtained by multispectral remote sensing with the vectorized topographic map as a base map, and select an obvious and clear positioning marker as a ground control point on the base map and the remote sensing image; the processor 30 is specifically configured to resample the remote sensing image by using a cubic interpolation method according to the position of each pixel on the output image in the input image, and establish a new remote sensing image.
For a specific process of interpolating three times in this embodiment, please refer to the related description of the previous method embodiment, which is not described herein again.
The processor 30 is specifically configured to perform noise reduction processing on the image data by using a Frost filter; the processor 30 is specifically configured to stretch the dynamic range of the gray value of the remote sensing image to a specified range according to a linear relation formula in a linear stretching manner, so as to increase the contrast of the remote sensing image.
The mathematical expression of the Frost filter in this embodiment specifically includes the following:
wherein,
in the above mathematical expression of this embodiment, g'(i,j)The pixel gray value is the pixel gray value after smoothing treatment; g(i,j)The original gray value of each pixel in the smooth window;the average value of the pixel gray levels in the window is obtained; m(i,j)The weight index of each corresponding pixel in the smoothing window; t is(i,j)The absolute distance from the central pixel to the adjacent pixels in the smoothing window is calculated; sigma(i,j)Is the variance of the pixel values in the smoothing window;is the size of the smoothing window; l is the imaging view.
As mentioned above, the transformation formula of the linear stretching includes:
wherein, G'(i,j)Is gray value with pixel coordinate of (i, j) after linear stretching treatment, G(i,j)For pixel seat after noise reduction treatmentGray value denoted (i, j), C0Is a constant value of C0May be determined from the overall actual image.
Please refer to fig. 5, fig. 5 is a general flowchart of the oyster rank area measurement and calculation system for aquaculture according to the present invention, where the oyster rank area measurement and calculation system of the present embodiment includes the processes of ground point selection, resampling, image denoising, linear stretching, rasterization, area calculation, etc. as described above, the calculation processing method related to any of the above embodiments may be specifically adopted, and is not described herein again.
According to the embodiment, automatic interpretation and calculation can be realized by processing the image data of the remote sensing image, the labor cost is reduced, and the calculation precision can be improved by geometric correction and deviation rectification.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. An oyster row area measuring and calculating method for aquaculture is characterized by comprising the following steps:
acquiring image data corresponding to the oyster arrangement area in a multispectral remote sensing mode;
performing geometric correction on the image data, and correcting the deformed image data;
carrying out image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects;
and processing the image data subjected to image enhancement processing to obtain a grid-form image, carrying out vectorization on the image, and selecting features related to the oyster arrangement area by using a connected spatial database to calculate the oyster arrangement area.
2. The method of claim 1, wherein the geometrically correcting the image data comprises: and performing geometric correction by selecting ground control points and image resampling.
3. The method for estimation according to claim 2, wherein the geometric correction by means of selection of ground control points and image resampling comprises:
taking the vectorized topographic map as a base map, carrying out comparative analysis on a remote sensing image of the image data acquired by multispectral remote sensing, and selecting an obvious and clear positioning marker on the base map and the remote sensing image as a ground control point;
and resampling the remote sensing image according to the position of each pixel on the output image in the input image, and establishing a new remote sensing image.
4. The method for measuring and calculating according to claim 3, wherein the resampling the remote sensing image according to the positions of the image elements on the output image in the input image to establish a new remote sensing image comprises: and processing by adopting a cubic interpolation method and utilizing a cubic polynomial to solve a mode of approximating to a theoretically optimal interpolation function.
5. The method according to claim 3 or 4, wherein the image enhancement processing on the corrected and rectified image data specifically comprises:
performing noise reduction processing on image data by using a Frost filter, wherein a mathematical expression of the Frost filter comprises the following steps:
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wherein,
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g′(i,j)the pixel gray value is the pixel gray value after smoothing treatment; g(i,j)The original gray value of each pixel in the smooth window;the average value of the pixel gray levels in the window is obtained; m(i,j)The weight index of each corresponding pixel in the smoothing window; t is(i,j)The absolute distance from the central pixel to the adjacent pixels in the smoothing window is calculated; sigma(i,j)For smoothing windowsVariance of pixel values in the mouth;is the size of the smoothing window;l is the imaging view.
6. The method of claim 5, wherein after performing noise reduction processing on the image data by using a Frost filter, the method further comprises:
and the dynamic range of the gray value of the remote sensing image is stretched to a specified range according to a linear relation formula in a linear stretching mode, so that the contrast of the remote sensing image is increased.
7. The method of claim 6, wherein the transformation formula of the linear stretching comprises:
<mrow> <msub> <msup> <mi>G</mi> <mo>,</mo> </msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mn>2</mn> </mfrac> <mo>+</mo> <mfrac> <mrow> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> </mrow> <mn>16</mn> </mfrac> <mo>+</mo> <msub> <mi>C</mi> <mn>0</mn> </msub> </mrow>
wherein, G'(i,j)Is gray value with pixel coordinate of (i, j) after linear stretching treatment, G(i,j)Is the gray value with the coordinate of (i, j) of the pixel after the noise reduction processing, C0Is a constant.
8. The oyster arrangement area measuring and calculating system for aquaculture is characterized by comprising a processor, wherein when the processor executes program instructions:
the processor is used for acquiring image data corresponding to the oyster arrangement area in a multispectral remote sensing mode;
the processor is used for carrying out geometric correction on the image data and rectifying the deviation of the deformed image data;
the processor is used for carrying out image enhancement processing on the corrected and rectified image data so as to highlight useful information related to oyster arrangement areas and enlarge direct characteristic differences of different objects;
the processor is used for processing the image data subjected to image enhancement processing to obtain a grid-form image, vectorizing the image, and selecting features related to the oyster arrangement area by using a connected spatial database to calculate the oyster arrangement area.
9. The system of claim 8, wherein:
the processor is specifically used for carrying out comparative analysis on the remote sensing image of the image data acquired by multispectral remote sensing by taking the vectorized topographic map as a base map, and selecting an obvious and clear positioning marker as a ground control point on the base map and the remote sensing image; the processor is specifically used for resampling the remote sensing image by adopting a cubic interpolation method according to the position of each pixel on the output image in the input image, and establishing a new remote sensing image;
the processor is specifically configured to perform noise reduction processing on the image data by using a Frost filter; the processor is specifically used for stretching the dynamic range of the gray value of the remote sensing image to a specified range according to a linear relation formula in a linear stretching mode, and increasing the contrast of the remote sensing image;
wherein, the mathematical expression of the Frost filter comprises the following:
<mrow> <msubsup> <mi>g</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&prime;</mo> </msubsup> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mover> <msub> <mi>g</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>&OverBar;</mo> </mover> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>C</mi> <mi>I</mi> </msub> <mo><</mo> <msub> <mi>C</mi> <mi>u</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>g</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>&times;</mo> <msub> <mi>M</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> </mrow> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>C</mi> <mi>u</mi> </msub> <mo>&le;</mo> <msub> <mi>C</mi> <mi>I</mi> </msub> <mo>&le;</mo> <msub> <mi>C</mi> <mi>max</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>g</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>C</mi> <mi>I</mi> </msub> <mo>></mo> <msub> <mi>C</mi> <mi>max</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
wherein,
<mrow> <msub> <mi>A</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>&sigma;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <msup> <mover> <msub> <mi>g</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>&OverBar;</mo> </mover> <mn>2</mn> </msup> </mfrac> </mrow>
g′(i,j)the pixel gray value is the pixel gray value after smoothing treatment; g(i,j)The original gray value of each pixel in the smooth window;the average value of the pixel gray levels in the window is obtained; m(i,j)The weight index of each corresponding pixel in the smoothing window; t is(i,j)The absolute distance from the central pixel to the adjacent pixels in the smoothing window is calculated; sigma(i,j)Is the variance of the pixel values in the smoothing window;is the size of the smoothing window;l is the imaging view.
10. The system of claim 9, wherein the linear stretch transformation equation comprises:
<mrow> <msub> <msup> <mi>G</mi> <mo>,</mo> </msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mn>2</mn> </mfrac> <mo>+</mo> <mfrac> <mrow> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msub> </mrow> <mn>16</mn> </mfrac> <mo>+</mo> <msub> <mi>C</mi> <mn>0</mn> </msub> </mrow>
wherein, G'(i,j)Is gray value with pixel coordinate of (i, j) after linear stretching treatment, G(i,j)Is the gray value with the coordinate of (i, j) of the pixel after the noise reduction processing, C0Is a constant.
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