CN113902635A - A kind of infrared thermal imager image processing method - Google Patents
A kind of infrared thermal imager image processing method Download PDFInfo
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Abstract
The invention discloses an infrared thermal imager image processing method, which comprises the steps of carrying out histogram equalization on an original infrared image to obtain a global gray image, carrying out histogram equalization on an original infrared image to obtain a local gray image, combining the local gray image and the global gray image, and mapping the local gray image and the global gray image into an enhanced gray image. The invention combines the global histogram and the local histogram, can obtain good contrast, can process scenes with large dynamic range of high-temperature objects, and can make up for the defect of insufficient contrast with low intensity.
Description
Technical Field
The invention belongs to the technical field of infrared imaging, and particularly relates to an infrared thermal imager image processing method under different environmental temperatures.
Background
Infrared radiation is present at object temperatures above absolute zero. The infrared imaging is just to convert radiation temperature difference of an object into a gray image which can be recognized by human eyes, so that the target can be stably measured without being influenced by ambient light. Because the infrared detection device is made of a heat-sensitive material, the infrared device generates heat, so that the infrared image has low general contrast and large noise, and the infrared image needs to be processed by adopting a histogram equalization technology and the like. The infrared focal plane receives the heat radiation and then outputs in a current or voltage mode through the photoelectric conversion circuit, the system converts the analog signal into a 16BIT digital signal, the image imaging processing converts the 16BIT digital signal into an 8BIT gray level image which can be recognized by human eyes, and in the mapping process, the process of compressing the dynamic azimuth in a high dynamic range is inevitably existed, so that the loss of the gray level of the image is inevitably caused, the visual effect of the image is reduced, and the infrared histogram processing can be adopted. The traditional histogram equalization technology dynamically and uniformly amplifies an original image according to the density of gray levels, so that the defects of over-explosion or under-explosion and the like cannot be avoided.
Disclosure of Invention
In view of this, the present invention provides a thermal infrared imager image processing method, which can improve the quality of an infrared image, aiming at the defects of the conventional infrared histogram processing.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an infrared thermal imager image processing method comprises the following steps:
s1, carrying out histogram equalization on the whole original infrared image to obtain a global gray image;
s2, carrying out histogram equalization on the original infrared image locally to obtain a local gray image;
and S3, combining the local gray image and the global gray image to map the local gray image and the global gray image into the gray image subjected to enhancement processing.
Further, step S1 includes:
(S11) acquiring a probability density function of the original infrared image;
(S12) solving a cumulative distribution function of the infrared image;
(S13) global histogram equalization is used for the entire original infrared image, and the 16-bit original image is mapped to an 8-bit grayscale image to obtain a global grayscale image.
Further, the step (S13) is followed by further comprising:
(S14) the gray scale with the distribution probability of 0 in the global gray scale image is removed, and global histogram equalization is performed again to obtain an enhanced global gray scale image.
Further, a global gray mapping offset value is firstly obtained, and then the gray of the gray 0 with the distribution probability is eliminated.
Further, step S2 includes:
(S21) calculating a mean value of the original infrared whole image;
(S22) dividing the original infrared image by using the average value as a threshold value to obtain a low-temperature part and a high-temperature part, merging the low-temperature part in the histogram of the original infrared image by using a low-temperature threshold value, and merging the high-temperature part by using a high-temperature threshold value;
(S23) according to the histogram after the layering processing, the gray level mapping from 16bit to 8bit is carried out on the original infrared image again to obtain a local gray level image.
Further, in step (S22), the low temperature histogram starts to approach the mean gray scale compression using the low temperature threshold, and the high temperature portion starts to approach the mean gray scale compression using the high temperature threshold.
Further, the step (S23) is followed by further comprising:
(S24) the local gray image is CLAHE histogram equalized to obtain an enhanced global gray image.
Further, between the step (S23) and the step (S24), the gradation having a distribution probability of 0 in the local image is first removed.
Further, in step (S24), local histogram enhancement conversion is performed using a fixed clipping factor.
Further, in step S3, the local grayscale image and the global grayscale image are synthesized by using the global factor to be mapped into the enhanced grayscale image, wherein the calculation model is:
g＝α*g1+(1-α)*g2
wherein g is the synthesized gray scale, α is the global factor, g1Is the gray scale of the global gray scale image, g2Is the gray scale of the local gray scale image.
Compared with the prior art, the invention provides an enhanced infrared histogram equalization technology aiming at the defects of the traditional infrared histogram processing image, which can well reserve the details of each gray level layer of the infrared image and ensure that the contrast of a plurality of areas of the image is improved differently.
Particularly, the invention adopts a mode of combining the global histogram with the local histogram, and can obtain good contrast on the basis of keeping the original histogram, thereby not only processing the scene with large dynamic range of the high-temperature object, but also making up the defect of insufficient contrast at low intensity. The method disclosed by the invention has the advantages that the gray scale of the image gray scale image mapped by the infrared radiation directly reflects the surface temperature of the object, and the temperature corresponding relation between 8 bits and 16 bits of the image gray scale is kept as much as possible.
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FIG. 1 is a flow chart of the thermal infrared imager image processing method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments, but it should not be construed that the scope of the present invention is limited to the embodiments described below.
Referring to fig. 1, a general flow of the thermal infrared imager image processing method of the present invention is shown, which is described in detail below.
And S1, carrying out histogram equalization on the whole original infrared image to obtain a global gray image.
In step S1, the method for global histogram equalization specifically includes:
(S11) a probability density function of the original infrared image is acquired.
(S12) solving a cumulative distribution function of the infrared image.
(S13) global histogram equalization is used for the entire original infrared image, and the 16-bit original image is mapped to an 8-bit grayscale image to obtain a global grayscale image.
(S14) removing the distribution probability of 0 in the (S13) global gray image, and then performing global histogram equalization again to obtain an enhanced global gray image.
In this step S1, since the infrared image is mostly applied in low gray scale, the direct global histogram will tend to cause low temperature noise amplification, and the noise amplification is caused by the large number of gray scale discontinuities in the low temperature region. Therefore, the invention makes a histogram on the mapped 8bit gray level image again to eliminate the part of which the distribution probability is 0. And then histogram mapping is carried out again, and the image gray scale is continuously and uniformly distributed in a small gray scale range. Since all high-temperature objects in the infrared image correspond to 255, the global gray-scale mapping offset value gray _ global _ offset is further obtained, and the gray scale after 0 is removed is calculated again, so as to obtain the gray-scale image G1 with enhanced global histogram equalization.
And S2, locally performing histogram equalization on the original infrared image to obtain a local gray level image.
In this step, a CLAHE local histogram is performed on the original infrared image, which further comprises the steps of:
(S21) solving a Mean value of the original infrared integral image;
(S22) the original infrared image is segmented using the mean value as a threshold to obtain a low-temperature part and a high-temperature part, and the low-temperature part in the histogram of the original infrared image is merged using a threshold th0 and the high-temperature part is merged using th 1.
(S23) the histogram after the layering processing is carried out, and the gray level mapping from 16bit to 8bit is carried out on the original infrared image again to obtain a local gray level image.
(S24) CLAHE histogram equalization is performed on the local gray image in the above (S23) to obtain an enhanced global gray image.
In this step, luminance limit adaptive histogram Conversion (CLAHE) is performed on the latest 8bit infrared gray image subjected to local histogram processing, and after CLAHE histogram equalization, an enhanced local gray image G2 is obtained.
And S3, combining the local gray image and the global gray image to map the local gray image and the global gray image into the gray image subjected to enhancement processing.
In step S3, the local grayscale image and the global grayscale image are synthesized using a global factor, and mapped to the enhanced grayscale image, where the global factor is α, and the model used is:
g＝α*g1+(1-α)*g2
wherein g is the sum gray, α is the global factor, g1Is the gray scale of the global gray scale image G1, G2Is the gray scale of the local gray scale image G2.
Therefore, by adopting a mode of combining the global histogram and the local histogram, on the basis of keeping the original histogram, good contrast can be obtained, so that the scene with a large dynamic range of a high-temperature object can be processed, and the defect of insufficient contrast at low intensity can be overcome. For the image gray level image mapped by the infrared radiation, the gray level directly reflects the surface temperature of an object, and the corresponding relation of the temperatures of 8bit and 16bit of the image gray level can be kept as much as possible.
In order to achieve the above technical effects, the technical solution of the present invention further adopts the following implementation procedures, which are specifically described below.
(1) Inputting an infrared image I, carrying out histogram statistics on the whole original 16bit infrared image, acquiring a frequency density function of the infrared original image, and carrying out normalization processing.
Wherein N is the total pixels of an image, which is the product of the image degree W and the height H of the image; r iskIs the kth gray level; n iskIs a gray level of rkThe number of pixels; and L is a gray scale.
(2) The Cumulative Distribution Function (CDF) of the 16bit infrared image is calculated, and the calculation model is as follows:
in the formula, C (r)k) Is the cumulative probability.
(3) And calculating a 0-to-L gray level conversion function to 0-255 gray level conversion function by using the cumulative distribution function as a conversion function, wherein the calculation model is as follows:
T(rk)＝[C(rk)*255]，k＝0,1,2,...L-1；0≤rk≤1
in the formula, T (r)k) Is a gray scale.
(4) Since low contrast objects in an infrared scene dominate the scene, using the cumulative distribution function directly as the gray scale conversion function, while providing overall contrast, necessarily amplifies low contrast noise, hence for T (r) abovek) The discontinuous part of the middle gray scale is processed, namely T (r)k) And eliminating the gray scales at the interval of 0 in the function, and combining the gray scales to obtain a new conversion function.
S(rk)＝{T(rj)},0≤j≤L1-1；
Wherein j is the gray level after merging; l1 is the gray scale after merging.
(5) A global gray scale bias is calculated.
gO＝255-L1
(6) A global gray map G1 is calculated.
G1(rk)＝S(rk)+gO
(7) And carrying out mean threshold processing on the original infrared image.
(8) The original 16bit histogram is layered using a threshold Mean, where different thresholds are used for the low and high temperature portions. Further, similar to step (4), the grays in the histogram smaller than the threshold are eliminated, and then combined before and after. To keep the low and high temperature part data excessively continuous, the low and high temperature histograms require the Mean gray scale to start, the low temperature histogram starts to approach the Mean gray scale compression using th0, and the high temperature part starts to approach the Mean gray scale compression using th 1.
In the formula, L0 is lower boundary gray after low-temperature gray is combined; l1 is the upper boundary gray level of the high temperature gray level merge; l is the original 16bit gray level.
(9) The P2 Cumulative Distribution Function (CDF) is calculated again, and the same processing method as in steps (2) and (3) can be specifically executed to calculate S (r) by performing 8-bit gray scale calculation on 16-bit raw datak)。
(10) The local histogram enhancement conversion is performed on the converted 8bit gray scale image using a luminance limited adaptive histogram (CLAHE) using a fixed clipping factor sigma.
G2(rk)＝f(S(rk),σ)
(11) And synthesizing a new gray-scale image by using the obtained globally and locally enhanced histograms.
G(ri)＝α*G1(ri)+(1-α)*G2(ri),(1≤i≤N)
Therefore, the invention can well reserve the details of each gray level layer of the infrared image by adopting the enhanced infrared histogram equalization technology, so that the contrast of a plurality of areas of the image can be improved differently.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that the scope of the present invention is not limited to the embodiments described above, and that various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the present invention.
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| CN116320501A (en) * | 2023-05-23 | 2023-06-23 | 浙江双视科技股份有限公司 | Infrared data compression method and readable storage medium |
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| WO2020224518A1 (en) * | 2019-05-06 | 2020-11-12 | 南京理工大学 | Self-adaptive infrared image defringe algorithm based on local median histogram |
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| WO2020224518A1 (en) * | 2019-05-06 | 2020-11-12 | 南京理工大学 | Self-adaptive infrared image defringe algorithm based on local median histogram |
| CN111583162A (en) * | 2020-05-07 | 2020-08-25 | 上海富瀚微电子股份有限公司 | Image enhancement method based on histogram equalization |
| CN111784609A (en) * | 2020-07-02 | 2020-10-16 | 烟台艾睿光电科技有限公司 | Image dynamic range compression method and device and computer readable storage medium |
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