CN111105371A - Low-contrast infrared image enhancement method - Google Patents
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Abstract
The invention provides a method for enhancing a low-contrast infrared image, which is used for evaluating the contrast of the infrared image by solving the standard deviation of the infrared image aiming at the image in a low-contrast scene, judging whether the method is applicable to the infrared image, mapping the infrared image conforming to the low contrast to a logarithmic domain for stretching treatment, and transforming again through an exponential domain. Judging whether the infrared image needs to be subjected to reverse color processing or not, performing mean value fuzzy processing on the infrared image, calculating an infrared image enhancement coefficient p, and calculating an enhancement parameter matrix by using the enhancement coefficient p, the infrared image before the fuzzy processing and the infrared image after the fuzzy processing to obtain a corresponding enhancement parameter A; and enhancing the infrared image before the blurring processing through the enhancement coefficient and the enhancement parameter matrix, and if the infrared image is subjected to the reverse color processing in the previous step, performing the reverse color processing again to obtain a final image. If the former step does not carry out the reverse color processing, the final image is obtained.
Description
Technical Field
The invention relates to the field of infrared image processing, in particular to a method for enhancing a low-contrast infrared image.
Background
With the development of science and technology, the application of infrared images is more and more extensive, and infrared imaging cannot be carried out when seeking dark covered dark. However, due to the problems of the detector or the environment, the obtained infrared images are also uneven, and valuable information is difficult to obtain in the infrared images with low contrast, so that the improvement of the quality of the images is very important. Histogram equalization is a common image enhancement method, can effectively improve the image contrast, but when more noise exists in an image, the corresponding noise is amplified, and when the gray value of the image is too concentrated, the histogram equalization cannot achieve a good effect; the genetic algorithm can perform self-adaptive enhancement aiming at the gray characteristic property of the infrared image, but the calculation amount is too long, and the real-time processing is difficult to realize under the condition of common calculation resources. Although researchers at home and abroad propose a plurality of infrared enhancement methods, the infrared enhancement method still has some defects, and the main problems are summarized as follows: the calculation amount of the algorithm is large, the real-time calculation is difficult to realize, the intelligence and the self-adaptability of the algorithm are poor, the algorithm needs to consider to set parameters, and the like.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of the prior art and provides a method for enhancing a low-contrast infrared image, which comprises the following steps: the method comprises the following steps:
step 1, acquiring an infrared image, judging whether the infrared image is a low-contrast image or not by solving a standard deviation of the image, if so, executing step 2, otherwise, not processing;
step 2, performing stretching transformation on the infrared image with low contrast in a logarithmic domain to obtain a stretched image;
step 3, calculating the gray level average value of the infrared image, judging whether reverse color processing is needed, if so, performing the reverse color processing, and then executing the step 4, and if not, directly executing the step 4;
step 4, calculating the standard deviation of the infrared image, and carrying out mean value filtering on the image;
step 5, calculating an enhancement coefficient to obtain a residual image;
and 6, obtaining a final enhanced image by utilizing the mapping relation between the residual image and the stretched image.
In step 1, the standard deviation of the image is calculated by the following formula:
wherein N is the pixel number of the acquired infrared Image1, mu is the average value of the gray value of the infrared Image pixels, sigma is the standard deviation of the calculated Image, and xiIndicating the gray scale value of the ith pixel of the infrared Image 1.
In step 1, if the standard deviation σ is smaller than 30, it is determined to be a low-contrast image.
In step 2, for the infrared Image1 meeting the requirement of low contrast, stretching is performed in a logarithmic domain by adopting the following formula:
wherein Pmax is the maximum value of the gray value in the infrared Image1, Average is the logarithmic mean value of all pixels in the infrared Image1 with e as the base, and the exponential of the logarithmic mean value with e as the base is calculated, and the calculation method comprises the following steps:
and delta is a minimum value, and is generally 0.0001, so that the situation that 0 is logarithmic is avoided, and Image1 is mapped to a logarithmic domain through the formula to be subjected to stretching transformation, so that a new infrared Image2 is obtained.
In step 3, the average value μ 2 of the gradations of the infrared Image2 is obtained, and if μ 2 is less than 128, the reverse color processing is required.
In step 3, the specific implementation method of the reverse color processing is to traverse the infrared Image2, and for each pixel gray Value, there are: value 255-Value.
In step 4, the specific implementation method for performing mean filtering on the image is as follows: and calculating the average value of the pixel gray value of the infrared Image and the pixel gray values of the eight neighborhoods thereof, and updating the pixel value of the infrared Image to be equal to the average value to obtain an Image 3.
The specific implementation method for calculating the enhancement coefficient in the step 5 comprises the following steps: and calculating the standard deviation sd of the infrared Image2, if the standard deviation sd is greater than 50, taking the value of sd as 50, calculating an infrared Image enhancement coefficient p, wherein p is 1-sd/50, traversing the infrared Image3, and multiplying each pixel gray value by the infrared Image enhancement coefficient p to obtain a new infrared Image 4.
In step 5, the specific implementation method of the residual image is as follows: traversing the infrared images Image2 and Image4, and for the same position, subtracting the pixel gray value of Image4 from the pixel gray value of Image2, and taking the value of the value smaller than 0 to obtain a residual Image 5.
In step 6, the specific implementation method for obtaining the enhanced image is as follows:
wherein, Value _ Image5(x) represents the pixel gray Value of the corresponding position of Image5, Value _ Image4(x) represents the Value of the corresponding position of Image4, F (x) is the enhanced pixel gray Value of the corresponding position of residual Image5, and the enhancement coefficient is Value _ Image5(x), and the enhancement coefficient is
Traversing Image5 to obtain an Image 6;
the coefficient A is calculated by the following method: traversing the infrared images Image2 and Image3 to obtain maximum pixel gray-scale values max2 and max3 respectively, and calculating a coefficient A to be (max2+ max 3)/2;
the obtained enhanced Image is referred to as Image6, and if the enhanced Image6 obtained in step 6 is subjected to the reverse color processing in step 3, the enhanced Image6 is subjected to the reverse color processing again to obtain a final enhanced Image.
Has the advantages that: the invention provides an image enhancement method aiming at an infrared image with low contrast, which comprises the steps of judging the image quality by utilizing the image variance, carrying out logarithmic domain stretching transformation on the infrared image which accords with the low contrast, and carrying out transformation again through an exponential domain. Judging whether the infrared image needs to be subjected to reverse color processing or not, performing mean value fuzzy processing on the infrared image, calculating an infrared image enhancement coefficient p, and calculating an enhancement parameter matrix by using the enhancement coefficient p, the infrared image before the fuzzy processing and the infrared image after the fuzzy processing to obtain a corresponding enhancement parameter A; and enhancing the infrared image before the blurring processing through the enhancement coefficient and the enhancement parameter matrix, and if the infrared image is subjected to the reverse color processing in the previous step, performing the reverse color processing again to obtain a final image. If the former step does not carry out reverse color processing, the obtained image is the final image, and the enhancement method of the invention has the characteristics of no parameter setting and obvious contrast stretching.
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The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of a low contrast infrared image enhancement method in an embodiment of the invention.
Fig. 2 is an infrared input image selected in an embodiment of the present invention.
Fig. 3 shows the result of the logarithmic domain and the exponential domain transformation performed on the infrared image according to the embodiment of the present invention.
Fig. 4 is the final enhanced image result obtained in the embodiment of the present invention.
Detailed Description
The invention provides a method for enhancing a low-contrast infrared image, which comprises the following steps:
a) infrared image data image1 is acquired as shown in fig. 2. And calculating the standard deviation of the image, wherein the smaller the standard deviation value is, the more concentrated the distribution of the image is, and the lower the contrast of the image is, judging whether the image belongs to the low-contrast infrared image or not by using the standard deviation of the image, and judging that the infrared image has low contrast and is considered to be the low-contrast infrared image when the standard deviation of the infrared image is less than 30 through data statistics. The standard deviation of the image is 8.45505, which meets the judgment condition of the low-contrast infrared image.
b) Traversing the infrared Image1, finding the value with the maximum gray value in the Image, which is recorded as Pmax, and the Pmax is 255. And calculating the logarithmic Average value of all pixels in the infrared image1 with the base e, and solving the exponential Average value of the logarithmic Average value with the base e. The calculation method comprises the following steps:
where δ is a minimum value to avoid the situation of taking the logarithm of 0. Traversing the infrared Image1 using the formula:
image1 is mapped to the logarithmic domain for stretch transformation to obtain a new infrared Image2, as shown in fig. 3.
c) The average value of the infrared Image2 is found to be 67.4991 and less than 128, and the infrared Image2 is subjected to reverse color processing, that is, the infrared Image2 is traversed, and the value is 255-value for each pixel value.
d) And carrying out mean value filtering processing on the infrared Image2 to obtain a new infrared Image 3.
e) And calculating the standard deviation sd of the infrared Image2 to be 16.7889, and if sd is greater than 50, the value of sd is 50, and calculating the infrared Image enhancement coefficient p to be 1-sd/50 to be 0.664222.
f) Traversing the infrared Image3, and multiplying each pixel gray value by the infrared Image enhancement coefficient p to obtain a new infrared Image 4.
g) Traversing the infrared images Image2 and Image4, and subtracting the value of Image4 from the value of Image2 at the same position, wherein all values smaller than 0 are 0, and obtaining a residual Image 5.
h) Traversing the infrared images Image2 and Image3, obtaining maximum values max2 and max3 respectively, and calculating the coefficient A as (max2+ max 3)/2.
i) The following expression is utilized:
and obtaining an enhanced infrared Image6, wherein Value _ Image5(x) represents the Value of the position corresponding to the Image5, and Value _ Image4(x) represents the Value of the position corresponding to the Image 4.
j) Because the reverse color processing is performed in step c), the infrared Image6 is subjected to the reverse color processing again to obtain a final enhanced Image, as shown in fig. 4.
The present invention provides a method for enhancing low-contrast infrared image, and a plurality of methods and approaches for implementing the method, and the above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, a plurality of modifications and embellishments can be made without departing from the principle of the present invention, and these modifications and embellishments should also be regarded as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. A method for enhancing a low-contrast infrared image comprises the following steps:
step 1, acquiring an infrared image, judging whether the infrared image is a low-contrast image or not by solving a standard deviation of the image, if so, executing step 2, otherwise, not processing;
step 2, performing stretching transformation on the infrared image with low contrast in a logarithmic domain to obtain a stretched image;
step 3, calculating the gray level average value of the infrared image, judging whether reverse color processing is needed, if so, performing the reverse color processing, and then executing the step 4, and if not, directly executing the step 4;
step 4, calculating the standard deviation of the infrared image, and carrying out mean value filtering on the image;
step 5, calculating an enhancement coefficient to obtain a residual image;
and 6, obtaining a final enhanced image by utilizing the mapping relation between the residual image and the stretched image.
2. The method of claim 1, wherein in step 1, the standard deviation of the image is found by the following formula:
wherein N is the pixel number of the acquired infrared Image1, mu is the average value of the gray value of the infrared Image pixels, sigma is the standard deviation of the calculated Image, and xiIndicating the gray scale value of the ith pixel of the infrared Image 1.
3. The method according to claim 2, wherein in step 1, if the standard deviation σ is less than 30, it is determined to be a low contrast image.
4. The method of claim 3, wherein in step 2, the infrared Image1 meeting the requirement of low contrast is stretched in a logarithmic domain by the following formula:
wherein Pmax is the maximum value of the gray value in the infrared Image1, Average is the logarithmic mean value of all pixels in the infrared Image1 with e as the base, and the exponential of the logarithmic mean value with e as the base is calculated, and the calculation method comprises the following steps:
wherein δ is a minimum value, Image1 is mapped to a logarithmic domain through the formula to be subjected to stretching transformation, and a new infrared Image2 is obtained.
5. The method according to claim 4, wherein in step 3, the average value μ 2 of the gray levels of the infrared Image2 is obtained, and if μ 2 is less than 128, the inverse color processing is required.
6. The method according to claim 5, wherein in step 3, the inverse color processing is specifically implemented by traversing the infrared Image2, and for each pixel gray Value, there are: value 255-Value.
7. The method according to claim 6, wherein in the step 4, the mean filtering of the image is implemented by: and calculating the average value of the pixel gray value of the infrared Image and the pixel gray values of the eight neighborhoods thereof, and updating the pixel value of the infrared Image to be equal to the average value to obtain an Image 3.
8. The method according to claim 7, wherein the specific implementation method for calculating the enhancement coefficient in step 5 is: and calculating the standard deviation sd of the infrared Image2, if the standard deviation sd is greater than 50, taking the value of sd as 50, calculating an infrared Image enhancement coefficient p, wherein p is 1-sd/50, traversing the infrared Image3, and multiplying each pixel gray value by the infrared Image enhancement coefficient p to obtain a new infrared Image 4.
9. The method according to claim 8, wherein in step 5, the residual image is implemented by: traversing the infrared images Image2 and Image4, and for the same position, subtracting the pixel gray value of Image4 from the pixel gray value of Image2, and taking the value of the value smaller than 0 to obtain a residual Image 5.
10. The method for enhancing the low-contrast infrared image according to claim 9, wherein in the step 6, the method for obtaining the enhanced image is specifically realized by:
wherein, Value _ Image5(x) represents the pixel gray Value of the corresponding position of Image5, Value _ Image4(x) represents the Value of the corresponding position of Image4, F (x) is the enhanced pixel gray Value of the corresponding position of residual Image5, and the enhancement coefficient is Value _ Image5(x), and the enhancement coefficient is
Traversing Image5 to obtain an Image 6;
the coefficient A is calculated by the following method: traversing the infrared images Image2 and Image3 to obtain maximum pixel gray-scale values max2 and max3 respectively, and calculating a coefficient A to be (max2+ max 3)/2;
the obtained enhanced Image is referred to as Image6, and if the enhanced Image6 obtained in step 6 is subjected to the reverse color processing in step 3, the enhanced Image6 is subjected to the reverse color processing again to obtain a final enhanced Image.
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| CN112561881A (en) * | 2020-12-16 | 2021-03-26 | 南京莱斯电子设备有限公司 | Infrared image self-adaptive data enhancement method based on evaluation model |
| WO2021114564A1 (en) * | 2019-12-10 | 2021-06-17 | 南京莱斯电子设备有限公司 | Enhancement method for low-contrast infrared image |
| CN116883279A (en) * | 2023-07-11 | 2023-10-13 | 北京龙知远科技发展有限公司 | Short wave infrared image enhancement method with low noise and high real-time performance |
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