CN112184601A - Method for enhancing vein image under near infrared light source by utilizing improved CLAHE algorithm - Google Patents
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Abstract
The invention provides a method for enhancing vein images under a near infrared light source by utilizing an improved CLAHE algorithm, which comprises the following steps: step 1: partitioning; step 2: calculating a histogram; and step 3: calculating the clipping threshold clip Limit: and 4, step 4: redistributing the pixel points; obtaining a histogram h' (x) after redistribution processing; and 5: histogram equalization; step 6: reconstructing a pixel point pre-output gray value; and 7: and reconstructing the gray value output by the pixel points. The image can be better in a strong dimming area or a bright area after the CLAHE image contrast enhancement algorithm is improved, and the characteristics of the vein area can be better emphasized in the vein image under a near infrared light source.
Description
Technical Field
The invention relates to a method for enhancing a vein image under a near infrared light source by utilizing an improved CLAHE algorithm, in particular to a method for adjusting gray value remapping distribution by introducing a sine function power exponential function.
Background
At present, most of researches on vein images are to highlight the structural features of veins by irradiating vein regions with near-infrared light sources and then adopting a filtering and contrast enhancing method.
Histogram equalization is the most common method of image contrast enhancement. Histogram equalization is mainly classified into a global method and a local method. The global enhancement method mainly achieves the purpose of contrast enhancement by modifying the distribution of the image histogram; the local enhancement method is mainly to define a local contrast in advance, and then enhance the local contrast to achieve the effect of enhancing image details, and the classical local histogram equalization technology includes Contrast Limited Adaptive Histogram Equalization (CLAHE), sub-block overlapping histogram equalization, and sub-block partial overlapping histogram equalization. The CLAHE method combines the advantages of adaptive histogram equalization and contrast limitation, is particularly suitable for low-contrast images, and is not complex in implementation process.
The general global histogram equalization algorithm has the problem that the histogram equalization result is not very balanced, the gray level of an equalized image is reduced, and certain details disappear; some images are equalized, and the contrast ratio is unnaturally excessively enhanced, so that the enhancement effect is not ideal. The local method comprehensively considers the position and the gray information of the pixel point, and the processing effect is often better than that of the global method. Classical local histogram equalization techniques include Contrast Limited Adaptive Histogram Equalization (CLAHE), subblock overlap histogram equalization, subblock partial overlap histogram equalization. The CLAHE method combines the advantages of adaptive histogram equalization and contrast limitation, is particularly suitable for low-contrast images, and is not complex in implementation process. However, when the CLAHE method is used for processing a vein image under a near-infrared light source, a strong dark area, namely a vein, cannot be better processed, so that vein features cannot be well highlighted when the image gray value is uniform.
Accordingly, there is a need for improvements in the art.
Disclosure of Invention
The invention aims to provide a method for enhancing vein images under a near infrared light source by using an improved CLAHE algorithm in an efficient manner.
In order to solve the technical problem, the invention provides a method for enhancing a vein image under a near infrared light source by using an improved CLAHE algorithm, which comprises the following steps:
step 1: partitioning; dividing an input image into c x r non-overlapping sub-blocks with equal size, wherein the number of pixels contained in each sub-block is M;
step 2: calculating a histogram; h (x) is used for representing the histogram of the sub-blocks, x represents the gray level, the value range is [0, L-1], and L is the possible gray level number;
and step 3: calculating the clipping threshold clip Limit:
in the formula: norm Clip Limit is the contrast enhancement amplitude value;
and 4, step 4: redistributing the pixel points; obtaining a histogram h' (x) after redistribution processing;
and 5: histogram equalization; performing histogram equalization processing on the histogram h' (x) subjected to redistribution processing, wherein an equalization result is represented by f (x);
step 6: reconstructing a pixel point pre-output gray value; obtaining the gray value of each pixel point of each sub-block according to the balance result f (x), and calculating the gray value g of each point in the pre-output image as a reference point;
and 7: and reconstructing the gray value output by the pixel points.
As an improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
in step 4:
for each sub-block, clipping the histogram h (x) of the sub-block by using the corresponding clipping threshold clip Limit, and uniformly redistributing the total difference value of the clipped pixels into each gray level of the histogram, wherein the clipping threshold clip Limit comprises the following steps:
avgBincr＝totalE/L
wherein, total E refers to the total difference value of pixels exceeding the clip threshold Limit; avg BIncr refers to the pixel value in the histogram that averages each gray level increase;
and repeating the distribution process until all the cut pixel points are distributed.
As a further improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
in step 4:
the histogram of h (x) after redistribution process is represented by h' (x), and then
As a further improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
in step 6:
the gray value g of each point in the pre-output image is calculated by using a bilinear interpolation technology.
As a further improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
in step 7:
according to the gray value g of each point in the pre-output image obtained in the step 6, the gray value g is remapped through a sine function power exponential function to obtain the gray output value f of each pixel point in the final image, the effect that the vein image further enhances and highlights the vein area is achieved, and the method comprises the following steps:
in the formula, alpha is a compression degree coefficient of a dark area, and the area mapping degree of the dark area is controlled during the image gray value remapping; beta is a compression degree coefficient of the bright area, and the area mapping degree of the bright area is controlled during the remapping of the image gray value; and (3) emphasizing the bright/dark area characteristics of the whole image by utilizing the adjustment of alpha and beta parameters through the image characteristics of the sine function power exponent function.
As a further improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
the contrast enhancement amplitude value norm Clip Limit is 4 to 15.
As a further improvement to the method of the present invention for enhancing vein images under near infrared light sources using the improved CLAHE algorithm:
the compression degree coefficient α of the dark region is 1.8, and the compression degree coefficient β of the bright region is 1.9.
The technical scheme of the invention is as follows: a method for adjusting gray value remapping distribution by introducing a sine function power exponential function when gray value remapping is carried out on the basis of CLAHE image enhancement; the method comprises the following steps:
the method comprises the following steps: and obtaining a gray level mapping image of each sub-block region counted by the CLAHE.
Step two: and setting a compression degree coefficient alpha of the dark area, and controlling the area mapping degree of the dark area when the image gray value is remapped.
Step three: and setting a compression degree coefficient beta of the bright area, and controlling the area mapping degree of the bright area when the image gray value is remapped.
Step four: introducing sine function power exponential function
And (4) remapping the gray value after image enhancement, wherein g is the gray value which is pre-output after the original CLAHE algorithm is processed.
For example, as shown in fig. 3, the improved vein image result pair is that a in fig. 3 is an original image, b in fig. 3 is a processing result after classical histogram equalization, c in fig. 3 is a processing result with interpolated CLAHE, and d in fig. 3 is a final processing result of CLAHE improved by the present invention.
The method for enhancing the vein image under the near infrared light source by improving the CLAHE algorithm has the technical advantages that:
the image can be better in the strong dim area or the bright area after the CLAHE image contrast enhancement algorithm is improved, and the characteristics of the vein area can be better emphasized in the vein image under the near infrared light source.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Figure 1 is the CLAHE algorithm. (a) Shearing a histogram; (b) a histogram after CLAHE redistribution;
FIG. 2 is a flow chart of image contrast enhancement by the modified CLAHE algorithm;
FIG. 3 is a modified vein image; a is original image, b is processed result after classical histogram equalization, c is processed result with interpolation CLAHE, d is improved CLAHE final processing result.
Detailed Description
The invention will be further described with reference to specific examples, but the scope of the invention is not limited thereto.
Example 1 method for enhancing vein images under near infrared light source using the modified CLAHE algorithm, as shown in figures 1-3,
the vein image vein area characteristic under the near-infrared light source is more obvious by performing the image enhancement method capable of emphasizing the controllable bright and dark areas by adjusting the image gray value remapping through CLAHE equalization.
CLAHE limits the enhancement amplitude of local contrast by limiting the height of the local histogram, thereby limiting the amplification of noise and excessive enhancement of local contrast. Firstly, dividing an image into a plurality of sub-blocks, then carrying out histogram 'shearing' on each sub-block (see (a) in figure 1), then carrying out histogram equalization on each sub-block (see (b) in figure 1), and finally carrying out interpolation operation on each pixel to obtain a transformed gray value, thereby realizing contrast-limited self-adaptive image enhancement.
As shown in fig. 2, a flowchart of an improved CLAHE algorithm mainly includes the following 7 steps:
step 1: and (5) partitioning. Dividing the input image into c x r non-overlapping sub-blocks with equal size, wherein each sub-block contains M pixels. The larger the sub-block, the more pronounced the enhancement effect, but the more image detail is lost.
Step 2: a histogram is calculated. The histogram of the sub-block is represented by h (x), x represents the gray level, and the value range is [0, L-1], and L is the possible number of 256 gray levels.
And step 3: calculating the clipping threshold clip Limit:
in the formula: norm Clip Limit is a contrast enhancement amplitude value that determines the amplitude of contrast enhancement, and is experimentally determined to be generally between 4 and 15.
And 4, step 4: and (6) redistributing the pixel points. For each sub-block, clipping the histogram h (x) of the sub-block by using the corresponding clipping threshold clip Limit, and uniformly redistributing the total difference value of the clipped pixels into each gray level of the histogram, wherein the clipping threshold clip Limit comprises the following steps:
avgBincr＝totalE/L
in the formula, total E refers to the total difference of pixels exceeding the clip threshold Limit. avg BIncr refers to the pixel value in the histogram that increases on average per gray level.
The above allocation process is repeated until all the cut pixel points are allocated, as shown in (b) of fig. 1. If h' (x) represents the histogram of h (x) after the redistribution process, there are
And 5: and (5) histogram equalization. Histogram equalization is performed on the histogram h' (x) after the reallocation processing, and the equalization result is represented by f (x).
Step 6: and (5) reconstructing the gray value pre-output by the pixel points. And (4) obtaining the gray values of the central pixel points of the sub-blocks according to the balance result f (x), using the gray values as reference points, and calculating the gray value g of each point in the pre-output image by adopting a bilinear interpolation technology.
And 7: and reconstructing the gray value output by the pixel points. According to the gray value g of each point in the pre-output image obtained in the step 6, the gray value g is remapped through a sine function power exponential function to obtain the gray output value f of each pixel point in the final image, the effect that the vein image further enhances and highlights the vein area is achieved, and the method comprises the following steps:
where α is a compression degree coefficient of the dark region, and controls the region mapping degree of the dark region when the image gray-scale value is remapped. Beta is the compression degree coefficient of the bright area, and the area mapping degree of the bright area is controlled when the image gray value is remapped. And (3) emphasizing the bright/dark area characteristics of the whole image by utilizing the adjustment of alpha and beta parameters through the image characteristics of the sine function power exponent function. The vein region characteristics are more highlighted by the experiment that alpha is 1.8 and beta is 1.9 under the irradiation of a near infrared environment light source.
Finally, it is also noted that the above-mentioned lists merely illustrate a few specific embodiments of the invention. It is obvious that the invention is not limited to the above embodiments, but that many variations are possible. All modifications which can be derived or suggested by a person skilled in the art from the disclosure of the present invention are to be considered within the scope of the invention.
Claims (7)
1. The method for enhancing the vein image under the near infrared light source by utilizing the improved CLAHE algorithm is characterized by comprising the following steps of: the method comprises the following steps:
step 1: partitioning; dividing an input image into c x r non-overlapping sub-blocks with equal size, wherein the number of pixels contained in each sub-block is M;
step 2: calculating a histogram; h (x) is used for representing the histogram of the sub-blocks, x represents the gray level, the value range is [0, L-1], and L is the possible gray level number;
and step 3: calculating the clipping threshold clip Limit:
in the formula: norm Clip Limit is the contrast enhancement amplitude value;
and 4, step 4: redistributing the pixel points; obtaining a histogram h' (x) after redistribution processing;
and 5: histogram equalization; performing histogram equalization processing on the histogram h' (x) subjected to redistribution processing, wherein an equalization result is represented by f (x);
step 6: reconstructing a pixel point pre-output gray value; obtaining the gray value of each pixel point of each sub-block according to the balance result f (x), and calculating the gray value g of each point in the pre-output image as a reference point;
and 7: and reconstructing the gray value output by the pixel points.
2. A method of enhancing a vein image under a near infrared light source using a modified CLAHE algorithm as claimed in claim 1, wherein:
in step 4:
for each sub-block, clipping the histogram h (x) of the sub-block by using the corresponding clipping threshold clip Limit, and uniformly redistributing the total difference value of the clipped pixels into each gray level of the histogram, wherein the clipping threshold clip Limit comprises the following steps:
avgBincr＝totalE/L
wherein, total E refers to the total difference value of pixels exceeding the clip threshold Limit; avg BIncr refers to the pixel value in the histogram that averages each gray level increase;
and repeating the distribution process until all the cut pixel points are distributed.
3. A method of enhancing a vein image under a near infrared light source using a modified CLAHE algorithm as claimed in claim 2, wherein:
in step 4:
the histogram of h (x) after redistribution process is represented by h' (x), and then
4. A method of enhancing a vein image under a near infrared light source using a modified CLAHE algorithm as claimed in claim 3, wherein:
in step 6:
the gray value g of each point in the pre-output image is calculated by using a bilinear interpolation technology.
5. Method for enhancing vein images under near infrared light sources with the improved CLAHE algorithm according to claim 4, characterized in that:
in step 7:
according to the gray value g of each point in the pre-output image obtained in the step 6, the gray value g is remapped through a sine function power exponential function to obtain the gray output value f of each pixel point in the final image, the effect that the vein image further enhances and highlights the vein area is achieved, and the method comprises the following steps:
in the formula, alpha is a compression degree coefficient of a dark area, and the area mapping degree of the dark area is controlled during the image gray value remapping; beta is a compression degree coefficient of the bright area, and the area mapping degree of the bright area is controlled during the remapping of the image gray value; and (3) emphasizing the bright/dark area characteristics of the whole image by utilizing the adjustment of alpha and beta parameters through the image characteristics of the sine function power exponent function.
6. Method for enhancing vein images under near infrared light sources with the improved CLAHE algorithm according to claim 5, characterized in that:
the contrast enhancement amplitude value norm Clip Limit is 4 to 15.
7. Method for enhancing vein images under near infrared light sources with the improved CLAHE algorithm according to claim 6, characterized in that:
the compression degree coefficient α of the dark region is 1.8, and the compression degree coefficient β of the bright region is 1.9.
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