CN112132748A - Processing method for infrared thermal imaging super-resolution - Google Patents
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Abstract
The invention relates to a processing method of infrared thermal imaging super-resolution, which comprises the following steps: building an infrared thermal imaging analysis platform, wherein the infrared thermal imaging analysis platform comprises a main controller, and an infrared detector, a storage module and a touch screen which are respectively connected with the main controller, and the infrared detector detects the temperature of a detected target and transmits the temperature to the main controller; the main controller converts the temperature into a low-resolution gray scale image, converts the low-resolution gray scale image into a high-resolution gray scale image through an optimized bilinear interpolation algorithm, enhances the visual effect of the image through gray scale piecewise linear mapping, and converts the high-resolution gray scale image with the enhanced visual effect of the image into a high-resolution pseudo-color image; and displaying the high-resolution pseudo-color image and the temperature of the measured target through a touch screen, and storing the high-resolution pseudo-color image and the temperature of the measured target in a memory. The method realizes image enhancement through an optimized bilinear interpolation algorithm, gray level piecewise linear mapping and pseudo-color processing, and obtains clear infrared thermal imaging with super-resolution.
Description
Technical Field
The invention relates to the technical field of infrared thermal imaging, in particular to a processing method for infrared thermal imaging super-resolution.
Background
All objects above absolute zero (-273 c) will emit infrared radiation. A technique of converting the temperature distribution of the surface of an object into an infrared thermography visible to the human eye using a special electronic device called an infrared thermal imaging technique and displaying the temperature distribution of the surface of the object in different colors is called an infrared thermal imaging technique. The infrared thermography can reflect the important information of the self state, particularly the surface state, of a measured object, can also provide the information of object radiance, abnormal state and the like which cannot be provided by a common visible light image, can quickly acquire the state and temperature information of the measured object by means of the infrared thermography, and can also record the infrared thermography for further analysis, so that the quality of the infrared thermography is the key of the application effect of the infrared thermography technology. However, the infrared thermography corresponds to the thermal distribution field on the surface of the object, and the thermal image distribution map of the infrared radiation of each part of the actual measured target object is lack of gradation and stereoscopic impression compared with a visible light image due to very weak signals; in addition, in the process of acquiring the infrared image, an optical system, photoelectric conversion, signal processing and other processes are required, so that the final infrared thermograph has the defects of poor contrast between the target and the background, fuzzy edge profile of the target, poor non-uniformity, low signal-to-noise ratio and the like. Although there is a method for image enhancement processing of imaging, the super-resolution of infrared thermal imaging can be achieved by increasing the data volume or improving the denoising technique and using a residual error network, but a certain amount of calculation is increased. Therefore, a low-calculation-amount infrared thermal imaging image enhancement processing method is needed to realize the super-resolution of infrared thermal imaging.
Disclosure of Invention
Technical problem to be solved
Based on the problems, the invention provides a processing method for infrared thermal imaging super-resolution, which realizes image enhancement through an optimized bilinear interpolation algorithm, gray scale piecewise linear mapping and pseudo color processing to obtain clear infrared thermal imaging with super-resolution.
(II) technical scheme
Based on the technical problem, the invention provides a processing method for infrared thermal imaging super-resolution, which comprises the following steps:
s1, constructing an infrared thermal imaging analysis platform, wherein the infrared thermal imaging analysis platform comprises a main controller, and an infrared detector, a storage module and a touch screen which are respectively connected with the main controller, and the infrared detector detects the temperature of a detected target and transmits the temperature to the main controller;
s2, the main controller converts the temperature into a low-resolution gray image, converts the low-resolution gray image into a high-resolution gray image through an optimized bilinear interpolation algorithm, enhances the visual effect of the image through gray scale piecewise linear mapping, and converts the high-resolution gray image with the enhanced visual effect of the image into a high-resolution pseudo-color image;
and S3, displaying the high-resolution pseudo-color image and the temperature of each pixel point of the detected target through a touch screen, and storing the temperatures in a memory.
Further, the optimized bilinear interpolation algorithm is as follows:
where Δ x is the distance of the interpolated pixel point from the low temperature point, Q1、Q2As original pixel point, QnAnd obtaining the interpolation pixel point result.
Further, the formula for calculating the gray scale piecewise linear mapping is as follows:
wherein, x is the original gray level, y is the gray level conversion value, i is the key adjustable value, and n is the indeterminate value.
Further, a conversion formula for converting the high-resolution gray-scale image after enhancing the visual effect of the image into a high-resolution pseudo-color image is as follows:
wherein, val is gray scale, and r, g, b are three components of red, green and blue respectively.
Further, the calculation formula of n is as follows:
if Tmax|-|Tmin|＞0.1(|Tmax|+|TminL) to obtain
If Tmax|-|Tmin|＜0.1(|Tmax|+|TminI), then have
Wherein N is the total number of pixels, is the average of all temperatures,
Tmax、Tminthe average, maximum and minimum values, respectively, of all temperatures.
Further, the low-resolution gray scale map is an 8-bit gray scale map with 8 × 8 resolution.
Further, the high resolution gray scale map is an 8-bit gray scale map with 477 × 477 resolution.
Further, the high-resolution pseudo color image is a 477 × 477 resolution 12-bit color picture.
Further, in step S1, the main controller includes an STM32F407 chip, the infrared detector includes a GY-AMG8833 IR 8 × 8 array thermometry infrared sensor, the touch screen is a TFTLCD capacitive touch screen with a resolution of 800 × 480, and the memory is an SD card.
Further, the infrared thermal imaging analysis platform in step S1 further includes a key receiving module, a STLINK download module, and an online debugging interface module, which are respectively connected to the main controller.
Further, the temperature ranges from 0 ℃ to 80 ℃.
(III) advantageous effects
The technical scheme of the invention has the following advantages:
(1) according to the invention, the low-resolution gray image is subjected to super-resolution processing through an optimized bilinear interpolation algorithm, the contrast is improved through gray scale piecewise linear mapping transformation and pseudo-color processing, and the visual effect of an imaging result is improved, so that image enhancement is realized, the resolution of the obtained infrared thermal imaging is higher, and the image is clearer;
(2) compared with the conventional bilinear interpolation algorithm, the optimized bilinear interpolation algorithm better conforms to the actual temperature change curve, has better imaging effect, smaller operand and better imaging effect than the conventional nearest interpolation and bicubic interpolation algorithm;
(3) the invention combines the gray processing algorithm with the embedded system to improve the gray piecewise linear mapping method, thereby improving the generalization processing capability of the image;
(4) in order to avoid that the change rule among pixel points is changed by pseudo-color processing, the interpolation processing is firstly carried out, and then the pseudo-color processing is carried out, so that the imaging effect is better, and the image is clearer.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic flow chart of a processing method for super-resolution infrared thermal imaging according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an IR thermography analysis platform according to an embodiment of the present invention;
FIG. 3 is a comparison diagram of interpolation processing or pseudo color processing according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a piecewise linear mapping of gray levels according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of pseudo color processing according to an embodiment of the invention;
fig. 6 is a sequence comparison diagram of interpolation processing and pseudo color processing according to the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The infrared thermal imaging super-resolution processing method, as shown in fig. 1, includes the following steps:
s1, building an infrared thermal imaging analysis platform, detecting the temperature of the detected target by an infrared detector, and transmitting the temperature to a main controller;
the infrared thermal imaging analysis platform is shown in fig. 2 and comprises a main controller, and an infrared detector, a storage module, a touch screen, a key receiving module, a STINK download and online debugging interface module which are respectively connected with the main controller; the infrared detector comprises a GY-AMG8833 IR 8 x 8 array temperature measurement infrared sensor, 64 floating point temperature values of the infrared detector are read, then analog-to-digital conversion is carried out, the GY-AMG8833 IR 8 x 8 infrared sensor is connected with the main controller through an IIC bus, the operating temperature and the temperature range of a measured object of the GY-AMG8833 IR 8 x 8 infrared sensor are both 0-80 ℃, the temperature output resolution is 0.25 ℃, and the temperature of the object selected by the scheme is 0-60 ℃; the main controller comprises an STM32F407 chip, wherein the STM32F407 chip is used for converting temperature into a standard video signal, realizing the conversion from the temperature to a gray scale image, converting a low-resolution gray scale image into a high-resolution gray scale image and converting the gray scale image into a pseudo-color image, and the STM32F407 is a 32-bit ARM Cortex-M4 processor which internally comprises an FSMC variable static memory controller and is specially used for reading and writing various ROMs; the touch screen is a 4.3-inch TFTLCD capacitive touch screen, the resolution is 800 x 480, the touch screen is connected with the outside in a 16-bit parallel mode, an FSMC interface of STM32F4 is used for controlling pictures with the resolution of 477x477 to be displayed on the upper portion of a TFTLCD display screen, and the temperature value and the memory state of a certain point are displayed on the lower portion of the TFTLCD display screen; the memory is an SD card, a FATFS file system is utilized to process files, and a plurality of files can be read, written and cut simultaneously; the KEY acceptance module includes a start KEY _ UP and selection KEYs KEY0, KEY1, KEY2, the selection KEYs being used to select a value of a certain pixel. The IIC bus is used to implement the communication function between the respective hardware.
S2, the main controller converts the temperature into a low-resolution gray image, converts the low-resolution gray image into a high-resolution gray image through an optimized bilinear interpolation algorithm, enhances the visual effect of the image through gray scale piecewise linear mapping, and converts the high-resolution gray image with the enhanced visual effect of the image into a high-resolution pseudo-color image; specifically, the method comprises the following steps:
s2.1, converting the temperature into a low-resolution gray scale image through an infrared signal processing module;
the internal infrared signal processing module transmits the temperature information received by the GY-AMG8833 IR 8X 8 infrared sensor to the STM32 through the IIC bus. In the measuring range of 0-60 degrees, STM32 corresponds 64 received temperature data to 256 gray levels, namely 0-255, and a gray map is obtained by utilizing a piecewise linear stretching algorithm. Due to the fact that the pixel resolution of the GY-AMG8833 IR 8X 8 array thermometric infrared sensor is low, the obtained 8-bit low-resolution gray-scale image with the 8X 8 resolution is low in resolution and needs to be improved in subsequent steps.
S2.2, converting the low-resolution gray scale image into a high-resolution gray scale image through an optimized bilinear interpolation amplification module;
the main method for image enhancement is to perform interpolation amplification processing, and through comparison, the interpolation effects of different interpolation methods on the infrared image are as follows: wavelet transformation, bilinear interpolation, bicubic interpolation, logarithmic interpolation, nearest neighbor interpolation. Although the wavelet transformation effect is good, the calculated amount and the calculation difficulty are far higher than those of the latter four interpolation methods, and in view of high algorithm efficiency of bilinear interpolation, the interpolation effect is only slightly lower than that of wavelet transformation, the image enhancement is performed by adopting a bilinear interpolation algorithm. In the traditional bilinear interpolation algorithm, Q is set as an original pixel point, and Q is set as an original pixel pointnAs a result of interpolating pixel points, QnThe calculation formula is shown as follows:
in consideration of the rule of the actual temperature change curve and the particularity of the infrared image, the scheme performs targeted optimization on the formula, the optimized calculation formula is as follows, delta x is the distance between an interpolation pixel point and a low temperature point, and weight coefficients are respectively
Wherein x is1、x2Distance between two temperature points, Q1、Q2Original pixel points corresponding to temperature points at two ends; compared with the original bilinear interpolation algorithm, the optimized algorithm has no evolution processing, so that the optimized algorithm further reduces the operation amount of image processing. Meanwhile, the optimized algorithm better conforms to the actual temperature change curve, and is similar to x2The imaging effect is better. The average peak signal-to-noise ratio of the bilinear interpolation algorithm is 33.56, the average peak signal-to-noise ratio of the optimized bilinear interpolation algorithm is 34.29, and the optimized bilinear interpolation algorithm has a better effect.
The optimized bilinear interpolation algorithm of the scheme is tested with the conventional nearest neighbor interpolation and bicubic interpolation algorithm, the following tables 1 and 2 respectively represent the peak signal-to-noise ratio and the average value of the peak signal-to-noise ratio of various image enhancement algorithms,
TABLE 1 Peak SNR for various image enhancement algorithms
TABLE 2 Peak SNR averages for various image enhancement algorithms
Therefore, the optimized bilinear interpolation algorithm is the best one of common interpolation algorithms for processing the infrared image, and the average value of the peak signal-to-noise ratio of the image enhancement algorithm is 1.6% larger than the conventional optimal value.
The effect of the optimized bilinear interpolation algorithm is adopted to compare (a), (c), (b) and (d) of fig. 3, and the low-resolution gray scale map is expanded into a 477 × 477 high-resolution gray scale map through the optimized bilinear interpolation algorithm, so that the resolution is enlarged, the image quality is improved, and the defect of low resolution of the infrared sensor is overcome.
S2.3, enhancing the visual effect of the image through a gray scale piecewise linear mapping module, and dividing the interpolated image into blue, green and red 3 parts according to the gray value of the pixel by adopting a transformation curve of a gray scale piecewise linear mapping function;
if the original gray-scale image obtained by interpolation amplification is directly output by pseudo-color processing, the contrast of the image is low, and the visual effect of the imaged picture is poor. The human visual characteristics are non-linear and anisotropic. The human eye tends to focus on the varying gray scale domain and is more sensitive to noise in the smooth segments of the image than in the particular segments, so that when the image is enhanced, the contrast is increased in some particular segments of the image and decreased in the smooth segments.
Relation between infrared thermal imaging temperature measurement precision and external environment influence: through analysis and research of influencing factors, the relation between the influence factors and the real temperature of the surface of the measured object is established, and a method for reducing temperature measurement errors is found so as to improve the reliability and accuracy of temperature measurement. The infrared temperature measurement precision is influenced by many factors, including the surface emissivity of the measured object and the test background, as well as the physical characteristics of the measured object, the internal structure and stability of the thermal infrared imager, the measurement distance coefficient, the operator and the operating environment. According to the requirements of different temperature measurement occasions on temperature measurement precision, the characteristics of the measured target object and the environment, such as the surface emissivity of the target and the ambient temperature of the measured target. The degree of influence of the above factors varies greatly with the variation of the measurement conditions, and it is necessary to accurately set each parameter value in order to ensure the measurement accuracy and the measurement reliability. Therefore, n is required to be determined according to the actual application environment.
The gray scale piecewise linear mapping is shown in fig. 4, where i is the key adjustable value (initial value 105), the vertical axis is the gray scale conversion result, the horizontal axis is the original gray scale, and n is the indeterminate value (value determined according to the actual application environment). In fig. 4, through the transformation process of gray scale piecewise linear mapping, the dynamic range of the gray scale value in the original image between 0 to i and (i + n) to 255 is increased, and the dynamic range of the gray scale value in the original image between i to (i + n) is reduced, so that the contrast in the whole range is enhanced.
Displaying green in the section from i to (i + n), displaying blue below i, displaying red above (i + n), wherein n is the width of the temperature displaying green in the gray scale map, the gray scale piecewise linear mapping is calculated by the following formula, x is the original gray scale, and y is the gray scale conversion value:
the image gray scale segmentation mapping is to convert each pixel gray scale value x in the original gray scale image into another gray scale value, namely a gray scale conversion value y, according to a specific mapping rule (an above formula), so as to achieve the purpose of enhancing the visual effect of the image. While the piecewise linear gray scale transformation can expand the gray scale range of useful information on an image and increase the contrast, the gray scale range of corresponding noise is finally compressed to a smaller area. In order to improve the visual effect of the image, the scheme provides a method for combining a gray processing algorithm and an embedded system to improve the algorithm. The image is preprocessed by a chip, the dynamic change of an indeterminate value n is realized to improve the generalization processing capability of the algorithm to the image, the proportion of a low-temperature part and a high-temperature part is enlarged, the gray scale proportion of an intermediate temperature is reduced, and the contrast is improved.
Let T be a series of measured temperature valuesmax、Tmin、
Maximum, minimum and mean values, respectively, for all temperatures:
I. if Tmax|-|Tmin|＞0.1(|Tmax|+|TminL) to obtain
II. If Tmax|-|Tmin|＜0.1(|Tmax|+|TminI), then have
Wherein N is the total number of pixels,
is an average value of the low-temperature portion,
is the average value of the high temperature part, and n is an environment-determined indefinite value; the transformation curve of the mapping function divides the original image into blue, green and red 3 parts according to the gray value of the pixel, in each part, the transformed gray value of the pixel keeps the original sequence, but the transformed gray value of the pixel is expanded to improve the contrast, thus, the contrast between the pixels of the corresponding 3 parts of gray is increased, namely, the proportion of the low-temperature part and the high-temperature part is expanded, the proportion of the gray of the intermediate temperature is reduced, and the imaging result is effectively improvedAnd (5) visual effect.
S2.4, realizing the conversion from the high-resolution gray-scale image to a pseudo-color image after the visual effect of the image is enhanced through a pseudo-color processing module;
the main purpose of the pseudo color processing is to improve the color contrast of the image to achieve the purpose of image enhancement. The image obtained by piecewise linear mapping has poor visual effect, the shape of a heat source in the image is difficult to recognize, and a pseudo-color treatment is required to obtain a predetermined imaging result. Since the resolving power of human eyes to color is far higher than that to gray scale, the gray scale image is converted into a color image representation sensitive to human eyes, thereby improving the visual imaging effect of the final image.
And mapping different gray levels in the gray level image to obtain corresponding colors. Different gray scale images can be converted into corresponding pseudo color images by setting different gray scale mapping functions. RGB can be mapped by converting temperature data into 256 gray scales, wherein different gray scales display different colors, and the lighter the gray scale, the closer the color is to blue, and the darker the gray scale, the closer the color is to red.
If the gray scale is val, and r, g, b are three components of red, green and blue:
the graph made according to the above formula is as shown in fig. 5, the pseudo-color processing maps each gray level to a different color by processing the gray level values of the pixels in each original image with three independent continuous transformations of r, g, b, respectively (see fig. 5), and the pixels with smaller gray levels will appear mainly blue, the pixels with larger gray levels will appear mainly red, and the pixels with intermediate gray levels will appear mainly green and less saturated according to the transformation functions of the above three formulas.
In this step, the 477 × 477 8-bit grayscale image is converted into 477 × 477 12-bit color picture, and the pseudo color processing is adopted to improve the color contrast of the image, so that the visual effect is better, and the image enhancement is realized, for example, as shown in (a) and (b), (c) and (d) of fig. 3.
Considering that the infrared image processing sequence of the algorithm is different and the imaging effect has a certain difference, the pseudo-color processing can change the change rule between the pixel points, so that the image should be interpolated before the pseudo-color processing.
The algorithm also has an influence on the final imaging result on the image processing sequence. The gray interpolation and the pseudo-color processing algorithms are used for analyzing the sequential processing sequence of the image, so that the optimal algorithm sequence is known, and the test results are shown in table 3:
TABLE 3 Algorithm to handle sequential differential differences
As can be seen from table 3, the signal-to-noise ratio of the gray scale interpolation amplification and the pseudo color processing is large, and as shown in fig. 6, the image obtained by the gray scale interpolation amplification and the pseudo color processing is clearer.
S3, displaying the high-resolution pseudo-color image and the temperature of each pixel point of the detected target through a TFTLCD (thin film transistor liquid crystal display), and storing the temperature in an SD card;
the value of a certain pixel point can be selected through the key receiving module and the touch screen, the temperature of the pixel point is displayed below the TFTLCD screen, and the actual refreshing frequency of the screen is about 2.4 Hz. The screen image contains temperature information stored in the SD card in BMP format.
In summary, the processing method and the analysis platform based on infrared thermal imaging super resolution have the following advantages:
(1) according to the invention, the low-resolution gray image is subjected to super-resolution processing through an optimized bilinear interpolation algorithm, the contrast is improved through gray scale piecewise linear mapping transformation and pseudo-color processing, and the visual effect of an imaging result is improved, so that image enhancement is realized, the resolution of the obtained infrared thermal imaging is higher, and the image is clearer;
(2) compared with the conventional bilinear interpolation algorithm, the optimized bilinear interpolation algorithm better conforms to the actual temperature change curve, has better imaging effect, smaller operand and better imaging effect than the conventional nearest interpolation and bicubic interpolation algorithm;
(3) the invention combines the gray processing algorithm with the embedded system to improve the gray piecewise linear mapping method, thereby improving the generalization processing capability of the image;
(4) in order to avoid that the change rule among pixel points is changed by pseudo-color processing, the interpolation processing is firstly carried out, and then the pseudo-color processing is carried out, so that the imaging effect is better, and the image is clearer.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A processing method for infrared thermal imaging super-resolution is characterized by comprising the following steps:
s1, constructing an infrared thermal imaging analysis platform, wherein the infrared thermal imaging analysis platform comprises a main controller, and an infrared detector, a storage module and a touch screen which are respectively connected with the main controller, and the infrared detector detects the temperature of a detected target and transmits the temperature to the main controller;
s2, the main controller converts the temperature into a low-resolution gray image, converts the low-resolution gray image into a high-resolution gray image through an optimized bilinear interpolation algorithm, enhances the visual effect of the image through gray scale piecewise linear mapping, and converts the high-resolution gray image with the enhanced visual effect of the image into a high-resolution pseudo-color image;
the optimized bilinear interpolation algorithm is as follows:
where Δ x is the distance of the interpolated pixel point from the low temperature point, x1、x2Distance between two temperature points, Q1、Q2For original pixel points corresponding to temperature points at both ends, QnThe interpolation pixel point result is obtained;
and S3, displaying the high-resolution pseudo-color image and the temperature of each pixel point of the detected target through a touch screen, and storing the temperatures in a memory.
2. The method for processing super resolution of infrared thermal imaging according to claim 1, wherein the formula for calculating the gray scale piecewise linear mapping is as follows:
wherein, x is the original gray level, y is the gray level conversion value, i is the key adjustable value, and n is the indeterminate value.
3. The processing method for super-resolution infrared thermal imaging according to claim 1, wherein the conversion formula for converting the high-resolution gray-scale image after enhancing the visual effect of the image into the high-resolution pseudo-color image is as follows:
wherein, val is gray scale, and r, g, b are three components of red, green and blue respectively.
4. The method for processing super-resolution of infrared thermal imaging according to claim 2, wherein the calculation formula of n is:
if Tmax|-|Tmin|＞0.1(|Tmax|+|TminL) to obtain
Wherein T isi<[|Tmax|-0.04(|Tmax|+|Tmin|)]；
Wherein
If Tmax|-|Tmin|＜0.1(|Tmax|+|TminI), then have
Wherein N is the total number of pixels, is the average of all temperatures,
Tmax、Tminthe average, maximum and minimum values, respectively, of all temperatures.
5. The method for processing super resolution in infrared thermal imaging according to claim 1, wherein the low resolution gray scale map is an 8-bit gray scale map with 8 x 8 resolution.
6. The method for processing infrared thermal imaging super resolution according to claim 1, wherein the high resolution gray scale map is an 8-bit gray scale map with 477x477 resolution.
7. The method for processing super resolution in infrared thermal imaging according to claim 1, wherein the high resolution pseudo color image is a 477x477 resolution 12-bit color picture.
8. The method for processing infrared thermal imaging super resolution as claimed in claim 1, wherein the main controller in step S1 includes an STM32F407 chip, the infrared detector includes GY-AMG8833 IR 8 × 8 array thermometric infrared sensors, the touch screen is a TFTLCD capacitive touch screen with a resolution of 800 × 480, and the memory is an SD card.
9. The method for processing super-resolution infrared thermal imaging according to claim 1, wherein the infrared thermal imaging analysis platform in step S1 further comprises a key receiving module, a STLINK download module and an online debugging interface module respectively connected to the main controller.
10. The method for processing super resolution of infrared thermal imaging according to claim 1, wherein the temperature is in the range of 0-80 ℃.
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