CN115272684A - Method for processing pseudo noise in vein image enhancement process - Google Patents
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Abstract
The invention relates to a method for processing pseudo noise in a vein image enhancement process, which belongs to the technical field of biological feature recognition and comprises the following steps: carrying out filtering processing in the cross symmetry direction on each pixel point in the vein image to obtain a filtering array of each pixel point; subdividing a small neighborhood through a large neighborhood and then judging the suspected noise point; further judging whether the pixel points of suspected noise are noise points by adopting a local processing method to obtain a noise point binary image; and taking the enhanced vein image as an input image to be filtered, taking the noise point binary image as a guide image, and outputting the filtered effect image by adopting a rapid guide filtering method. The method for processing the pseudo noise in the vein image enhancement process is a rapid local denoising method, can well solve local texture noise, avoids the formation of the pseudo noise, and avoids the formation of recognition and false recognition.
Description
Technical Field
The invention belongs to the technical field of biological feature recognition, and particularly relates to a method for processing pseudo noise in a vein image enhancement process.
Background
The image enhancement technology is one of key technologies in the field of image processing, can improve and improve the quality of an original image, and particularly can effectively improve the recognition rate and reduce the false recognition rate by enhancing a near-infrared vein image in a biological feature recognition-vein recognition neighborhood.
A conventional enhancement processing method for vein images, such as an image enhancement processing method for palm vein images disclosed in CN112308044B, includes: processing the collected palm vein image into a binary black-and-white image; determining a first finger seam point and a second finger seam point in the palm according to the binary black-and-white image; performing extension treatment on the first finger seam point and the second finger seam point to obtain a third finger seam point and a fourth finger seam point, and obtaining position information of an interested area according to the third finger seam point and the fourth finger seam point; obtaining an interested area image from the palm vein image according to the interested area position information; and performing enhancement processing on the image of the region of interest to obtain the enhanced image of the region of interest.
However, since the enhancement of the vein image and the simultaneous enhancement of the peripheral fine texture form the pseudo noise, it is an urgent problem to avoid the formation of the pseudo noise by weakening the edge texture noise while enhancing the vein feature.
Disclosure of Invention
The invention provides a method for processing pseudo noise in a vein image enhancement process, which aims to solve the problems that the pseudo noise is easy to generate and the identification and the recognition are increased when the existing image is enhanced.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
the invention relates to a method for processing pseudo noise in a vein image enhancement process, which comprises the following steps:
s1, filtering each pixel point in the vein image in a cross symmetrical direction to obtain a filtering array of each pixel point;
s2, setting a threshold value of the minimum value of the vein image gray level;
s3, solving a 5 × 5 neighborhood gray average value and a 3 × 3 neighborhood gray minimum value of a certain pixel point of the vein image;
s4, counting the number of the pixel points in the filter array of the pixel points, wherein the number of the pixel points is larger than the minimum value of the 3 × 3 neighborhood gray scale, comparing the minimum value of the 3 × 3 neighborhood gray scale with a threshold value, and further judging whether the pixel points are suspected noise pixel points or not;
s5, repeating the steps S3-S4 to obtain all the suspected noise pixel points, further judging whether the suspected noise pixel points are noise points or not by adopting a local processing method, assigning the gray values of the judged noise points as 1, assigning the gray values of the rest pixel points as 0, and obtaining a noise point binary image;
and S6, taking the enhanced vein image as an input image to be filtered, taking the noise point binary image as a guide image, and outputting a filtered effect image by adopting a rapid guide filtering method.
Preferably, the filtering array of the pixel point (i, j) in step S1 is z (k), k =0,1,2 \82307, and the expression of each array is:
wherein I and j are respectively row coordinates and column coordinates of the pixel points, I is a gray value of the pixel points, k is an index of the filter array, c, b and a respectively represent filter direction coefficients, c belongs to < -5 > -1 >, b belongs to < -3 > 0, and c belongs to (0, 2).
Preferably, the threshold of the minimum value of the 3 × 3 neighborhood gray level set in step S2 is mx, mx belongs to [85,125], the average value of the 5 × 5 neighborhood gray levels of the pixel points obtained in step S3 is Mean _ I (I, j), and the minimum value of the 3 × 3 neighborhood gray level obtained is MinI (I, j); a variable ks is also set in the step S3, and the initial value of the variable ks is 0;
the step S4 of determining whether the pixel is a suspected-noise pixel includes the specific steps of:
s4.1, judging whether the numerical value in the pixel point filter array is larger than the 3 × 3 neighborhood gray minimum value Mini (i, j), if so, adding 1 to the variable ks, otherwise, keeping the variable ks unchanged;
and S4.2, if the final variable ks is not less than 6 and the minimum value Mini (i, j) of the 3 x 3 neighborhood gray levels of the pixel point is greater than a threshold value mx, determining that the pixel point is a suspected-noise pixel point.
Preferably, the step S5 of further determining whether the pixel point of the suspected noise is a noise point by using a local processing method includes the specific steps of:
s5.1, setting a variable km1 and a variable km2, wherein the initial values of the variable km1 and the variable km2 are both 0;
s5.2, subdividing 7 × 7 neighborhoods of the suspected-noise pixel points into six sub-areas of 3 × 3, and calculating the accumulated gray value of the pixel points in each sub-area;
s5.3, judging whether the average value Mean _ I (I, j) of 5-to-5 neighborhoods of the suspected-noise pixel points is smaller than the accumulated gray value of the pixel points in the sub-area corresponding to the pixel points or not for each suspected-noise pixel point, if so, adding 1 to a variable km1, and if not, keeping the variable km1 unchanged;
s5.4, judging whether the gray value of each suspected noise pixel point is the maximum gray value of the sub area corresponding to the suspected noise pixel point, if so, adding 1 to a variable km2, and if not, keeping the variable km2 unchanged;
and S5.5, when the final value of the variable km1 is not less than the threshold km1 'of the variable km1, and the final value of the variable km2 is not less than the threshold km2' of the variable km2, the km1 'is in an element of [0,6], and the km2' is in an element of [0,6], determining the pixel point of the suspected noise as a noise point, assigning the gray value of the noise point as 1, and otherwise, assigning the gray value as 0, thereby obtaining a noise point binary image.
Preferably, the threshold km1 'of the variable km1 is 4, and the threshold km2' of the variable km2 is 2.
Preferably, the vein image enhanced in step S6 is a vein image enhanced by Gabor filtering; the kernel function of the Gabor filter is as follows:
in the kernel function of the Gabor filter,xis the pixel point row coordinate and is,yis the pixel point column coordinate and is,λgamma, which is the wavelength of the Gabor filter, is the spatial aspect ratio, representing the ellipticity of the Gabor filter,ψ a to be the direction of the filtering,σthe standard deviation is used as the standard deviation,x’for rotating pixel pointsψ a The row coordinate of the angle is set to,y’for rotating pixel pointsψ a Column coordinates of the angle;
wherein, the first and the second end of the pipe are connected with each other,
afor the purpose of filtering at different scales of the image,a=0,1,2…7；
rotation of pixelψ a Line coordinate of angle
And the pixel point rotatesψ a Column coordinate of angle
Satisfy formula (11)
Preferably, the method for enhancing the vein image in step S6 includes the following steps:
(1) Traversing each pixel point of the vein image I, and carrying out Gabor filtering processing of 8 different scales on each pixel point, wherein the filtering formula is as follows:
in the formula, the content of the active carbon is shown in the specification,I a is a pixel point (x，y) In thatψ a The gray value of the filtering direction after Gabor filtering processing;
(2) And obtaining the image I' after the Gabor filtering enhancement by taking the minimum gray values in 8 directions.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the method for processing the pseudo noise in the vein image enhancement process searches suspected noise points from cross-symmetric multidirectional filtering, subdivides a large neighborhood into small neighborhoods, further judges whether pixel points of the suspected noise are noise points by adopting a local processing method, outputs an accurate noise binary image, and filters the noise by quickly guiding a filtering method, so that the method is a quick local denoising method, can well solve local texture noise, avoids the formation of pseudo noise, and avoids the formation of recognition and false recognition.
Drawings
FIG. 1 is an original vein image;
FIG. 2 is a Gabor filtered enhanced image I';
fig. 3 is a diagram of the filtered results output by the fast-guided filtering method.
Detailed Description
For further understanding of the present invention, the present invention will be described in detail with reference to examples, which are provided for illustration of the present invention but are not intended to limit the scope of the present invention.
The invention relates to a method for processing pseudo noise in a vein image enhancement process, which comprises the following steps:
s1, filtering each pixel point in the vein image in a cross symmetrical direction to obtain a filtering array of each pixel point, wherein the specific mode is as follows:
for each pixel point (i, j) in the image, designing a filtering direction in a cross symmetry direction, and assuming that a filtering array in the cross symmetry direction is z (k), k =0,1,2 \82307, 7, wherein the expression of each array is as follows:
wherein I and j are respectively row coordinates and column coordinates of the pixel points, I is a gray value of the pixel points, k is an index of the filter array, c, b and a respectively represent filter direction coefficients, c belongs to-5, -1, b belongs to-3 and 0, and c belongs to (0 and 2).
S2, setting a threshold value of the minimum value of the vein image gray level: and setting a threshold value of the minimum value of the 3 x 3 neighborhood gray scales as mx, wherein mx belongs to [85,125].
S3, solving a 5 × 5 neighborhood gray level average value and a 3 × 3 neighborhood gray level minimum value of a certain pixel point of the vein image, wherein the 5 × 5 neighborhood gray level average value is Mean _ I (I, j), and the 3 × 3 neighborhood gray level minimum value is Mini (I, j); a variable ks is set, and the initial value of the variable ks is 0.
S4, counting the number of the pixel points with the value larger than the minimum value of the 3 × 3 neighborhood gray scale in the filter array of the pixel points, comparing the minimum value of the 3 × 3 neighborhood gray scale with a threshold value, judging whether the pixel points are suspected noise pixel points or not, and judging whether the pixel points are the suspected noise pixel points or not specifically comprises the following steps:
s4.1, judging whether the numerical value in the pixel point filter array is larger than a 3 × 3 neighborhood gray minimum value Mini (i, j), if so, adding 1 to a variable ks, and if not, keeping the variable ks unchanged;
and S4.2, if the final variable ks is not less than 6 and the minimum value Mini (i, j) of the 3 x 3 neighborhood gray levels of the pixel point is greater than a threshold value mx, determining that the pixel point is a suspected-noise pixel point.
S5, repeating the steps S3-S4 to obtain all the pixels of the suspected noise, further judging whether the pixels of the suspected noise are noise points by adopting a local processing method, assigning the gray value of the judged noise points as 1, assigning the gray values of the other pixels as 0, and obtaining a noise point binary image, wherein the specific steps are as follows:
s5.1, setting a variable km1 and a variable km2, wherein the initial values of the variable km1 and the variable km2 are both 0;
s5.2, subdividing 7 × 7 neighborhoods of the pixels of each suspected noise into six sub-areas of 3 × 3, and calculating the accumulated gray value of the pixels in each sub-area;
s5.3, judging whether the average value Mean _ I (I, j) of 5-to-5 neighborhoods of the suspected-noise pixel points is smaller than the accumulated gray value of the pixel points in the sub-area corresponding to the pixel points or not for each suspected-noise pixel point, if so, adding 1 to a variable km1, and if not, keeping the variable km1 unchanged;
s5.4, judging whether the gray value of each suspected noise pixel point is the maximum gray value of the sub-area corresponding to the suspected noise pixel point, if so, adding 1 to a variable km2, and if not, keeping the variable km2 unchanged;
and S5.5, when the final value of the variable km1 is not less than the threshold km1 'of the variable km1, and the final value of the variable km2 is not less than the threshold km2' of the variable km2, the km1 'is in an E [0,6], and the km2' is in an E [0,6], determining the pixel point of the suspected noise as a noise point, assigning the gray value of the noise point as 1, and otherwise, assigning the gray value as 0 to obtain a noise point binary image, wherein in the embodiment, the threshold km1 'is 4, and the threshold km2' is 2.
S6, taking the enhanced vein image as an input image to be filtered, taking the noise point binary image as a guide image, and outputting a filtered effect image by adopting a rapid guide filtering method;
in this embodiment, a Gabor filter is used to enhance an original vein image, and a kernel function of the Gabor filter is:
in the kernel function of the Gabor filter,xis the pixel point row coordinate and is,yis the pixel point column coordinate and is,λgamma, which is the wavelength of the Gabor filter, is the spatial aspect ratio, representing the ellipticity of the Gabor filter,ψ a in order to be the direction of the filtering,σthe standard deviation is used as the standard deviation,x’for rotating pixel pointsψ a The row coordinate of the angle is set to,y’for rotating pixel pointsψ a Column coordinates of the angle;
wherein, the first and the second end of the pipe are connected with each other,
afor the purpose of filtering at different scales of the image,a=0,1,2…7；
pixel rotationψ a Line coordinate of angle
And the pixel point rotatesψ a Column coordinate of angle
Satisfy formula (11)
The specific steps of adopting Gabor filtering to enhance the original vein image are as follows:
(1) Inputting an original vein image as shown in figure 1; traversing each pixel point of the vein image I, and carrying out Gabor filtering processing of 8 different scales on each pixel point, wherein the filtering formula is as follows:
in the formula, the first step is that,I a is a pixel point (x，y) In thatψ a The gray value of the filtering direction after Gabor filtering processing;
(2) The minimum gray values in 8 directions are taken to obtain the image I' after the enhancement by Gabor filtering as shown in fig. 2.
The filtered result output by the fast-guided filtering method is shown in fig. 3.
The present invention has been described in detail with reference to the embodiments, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (7)
1. A method for processing pseudo noise in a vein image enhancement process is characterized in that: which comprises the following steps:
s1, carrying out filtering processing in a cross symmetry direction on each pixel point in the vein image to obtain a filtering array of each pixel point;
s2, setting a threshold value of the minimum value of the vein image gray level;
s3, solving a 5 × 5 neighborhood gray average value and a 3 × 3 neighborhood gray minimum value of a certain pixel point of the vein image;
s4, counting the number of the pixel points with the value larger than the 3 × 3 neighborhood gray minimum value in the filter array of the pixel points, comparing the 3 × 3 neighborhood gray minimum value with a threshold value, and further judging whether the pixel points are suspected noise pixel points or not;
s5, repeating the steps S3 to S4 to obtain all the pixels of the suspected noise, further judging whether the pixels of the suspected noise are noise points by adopting a local processing method, assigning the gray value of the judged noise points as 1, and assigning the gray values of the other pixels as 0 to obtain a noise point binary image;
and S6, taking the enhanced vein image as an input image to be filtered, taking the noise point binary image as a guide image, and outputting the filtered effect image by adopting a rapid guide filtering method.
2. The method for processing pseudo noise in the process of enhancing the vein image according to claim 1, wherein: in the step S1, a filter array of the pixel points (i, j) is z (k), k =0,1,2 \82307, and an expression of each array is as follows:
wherein I and j are respectively row coordinates and column coordinates of the pixel points, I is a gray value of the pixel points, k is an index of the filter array, c, b and a respectively represent filter direction coefficients, c belongs to < -5 > -1 >, b belongs to < -3 > 0, and c belongs to (0, 2).
3. The method for processing the pseudo noise in the vein image enhancement process according to claim 2, wherein: the threshold value of the minimum value of the 3 × 3 neighborhood gray scale set in the step S2 is mx, mx belongs to [85,125], the average value of the 5 × 5 neighborhood gray scale of the pixel point obtained in the step S3 is Mean _ I (I, j), and the minimum value of the 3 × 3 neighborhood gray scale obtained is MinI (I, j); a variable ks is also set in the step S3, and the initial value of the variable ks is 0;
the step S4 of determining whether the pixel point is a suspected noise pixel point includes the specific steps of:
s4.1, judging whether the numerical value in the pixel point filter array is larger than the 3 × 3 neighborhood gray minimum value Mini (i, j), if so, adding 1 to the variable ks, otherwise, keeping the variable ks unchanged;
and S4.2, if the final variable ks is not less than 6 and the minimum value Mini (i, j) of the 3 x 3 neighborhood gray levels of the pixel point is greater than a threshold value mx, determining that the pixel point is a suspected-noise pixel point.
4. The method for processing pseudo noise in the vein image enhancement process according to claim 3, wherein: the step S5 of further determining whether the suspected noise pixel is a noise pixel by using a local processing method includes the specific steps of:
s5.1, setting a variable km1 and a variable km2, wherein the initial values of the variable km1 and the variable km2 are both 0;
s5.2, subdividing 7 × 7 neighborhoods of the suspected-noise pixel points into six sub-areas of 3 × 3, and calculating the accumulated gray value of the pixel points in each sub-area;
s5.3, judging whether the average Mean _ I (I, j) of 5-by-5 neighborhoods of the suspected-noise pixel points is smaller than the accumulated gray value of the pixel points in the sub-area corresponding to the pixel points or not for each suspected-noise pixel point, if so, adding 1 to the variable km1, and if not, keeping the variable km1 unchanged;
s5.4, judging whether the gray value of each suspected noise pixel point is the maximum gray value of the sub area corresponding to the suspected noise pixel point, if so, adding 1 to a variable km2, and if not, keeping the variable km2 unchanged;
and S5.5, when the final value of the variable km1 is not less than the threshold km1 'of the variable km1, and the final value of the variable km2 is not less than the threshold km2' of the variable km2, the km1 'is in an element of [0,6], and the km2' is in an element of [0,6], determining the pixel point of the suspected noise as a noise point, assigning the gray value of the noise point as 1, and otherwise, assigning the gray value as 0, thereby obtaining a noise point binary image.
5. The method for processing pseudo noise in the process of enhancing the vein image according to claim 4, wherein: the threshold value km1 'of the variable km1 is 4, and the threshold value km2' of the variable km2 is 2.
6. The method for processing pseudo noise in the process of enhancing the vein image according to claim 1, wherein: the vein image enhanced in the step S6 is a vein image enhanced by Gabor filtering; the kernel function of the Gabor filter is as follows:
in the kernel function of the Gabor filter,xis the pixel point row coordinate and is,yis the coordinates of the pixel point column,λgamma, which is the wavelength of the Gabor filter, is the spatial aspect ratio, representing the ellipticity of the Gabor filter,ψ a in order to be the direction of the filtering,σis the standard deviation of the measured data to be measured,x’for rotating pixel pointsψ a Angle of rotationThe row coordinates of (a) are set,y’for rotating pixel pointsψ a Column coordinates of the angle;
wherein the content of the first and second substances,
afor the purpose of filtering at different scales of the image,a=0,1,2…7；
rotation of pixelψ a Line coordinate of angle
And the pixel point rotatesψ a Column coordinates of angles
Satisfy formula (11)
7. The method for processing pseudo noise in the process of enhancing the vein image according to claim 6, wherein: the method for enhancing the vein image in the step S6 comprises the following steps:
(1) Traversing each pixel point of the vein image I, and carrying out Gabor filtering processing of 8 different scales on each pixel point, wherein the filtering formula is as follows:
in the formula, the content of the active carbon is shown in the specification,I a is a pixel point (x，y) In thatψ a The gray value of the filtering direction after Gabor filtering processing;
(2) And obtaining the image I' after the Gabor filtering enhancement by taking the minimum gray values in 8 directions.
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