Data processing method and system for infrared thermal imaging

Abstract

The invention discloses a data processing method and system for infrared thermal imaging. The method comprises the following steps: acquiring infrared thermal imaging information; performing noise reduction operation according to the infrared thermal imaging information to obtain a de-noised wavelet coefficient value; performing threshold function construction operation according to the de-noising wavelet coefficient value to obtain a local weight threshold function and a high threshold function; performing image reconstruction operation according to the de-noising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; and performing image output operation according to the second enhanced image to obtain an output image. According to the method, image details can be kept, and noise can be smoothed.

Description

Technical Field

[0001] The present invention relates to the field of infrared thermal imaging technology, and in particular to a data processing method and system for infrared thermal imaging. Background Art

[0002] At present, the application of infrared thermal imaging technology in the field of industrial detection continues to deepen, and its unique advantages make it an important tool for industrial safety monitoring and equipment maintenance. By capturing infrared radiation on the surface of an object, this technology can display the temperature distribution in real time and accurately, thereby helping engineers to promptly discover potential faults and safety hazards. In the power industry, infrared thermal imaging is widely used to detect temperature anomalies in equipment such as high-voltage cables, transformers, and switchgear. It can perform non-destructive testing without power outages, effectively preventing electrical fires and equipment overheating. In the production of electronic components, infrared thermal imaging can quickly identify problems such as poor welding or inconsistent materials, and improve product quality and production efficiency. The key to the application of thermal infrared imaging lies in how to extract more valuable information from thermal infrared images. Among them, image enhancement technology is a basic image processing technology that can improve the edges and detail features of thermal infrared images, which is conducive to analysts to make more detailed observations.

[0003] In one prior art, there are two methods: local enhancement and global enhancement. The global enhancement method, histogram equalization, enhances the contrast by adjusting the grayscale distribution of the entire image, but this method easily leads to blurring of image details. The local enhancement method can better highlight the detailed features of the image. Common local enhancement techniques include enhancement based on local standard deviation, gradient enhancement, Retinex transformation, and local Wiener filtering. These methods can effectively suppress noise and improve image contrast by processing in local areas of the image, but they also have some limitations, and the image details will be further blurred when processing noise. In addition, the noise problem of infrared images is also an important factor affecting its application effect. The noise mainly comes from electronic equipment, signal processing systems, and the external environment. In order to suppress noise, methods such as low-pass filters and median filters are used, but these methods will reduce the detailed information of the image while denoising.

[0004] In summary, the prior art has the problem of being unable to achieve noise smoothing while maintaining image details. Summary of the invention

[0005] The present invention provides a data processing method and system for infrared thermal imaging, so as to realize noise smoothing while maintaining image details.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a data processing method for infrared thermal imaging, comprising: Obtain infrared thermal imaging information; Perform a noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; According to the denoising wavelet coefficient value, a threshold function construction operation is performed to obtain a local weight threshold function and a high threshold function; Performing an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; Performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; Performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; An image output operation is performed according to the second enhanced image to obtain an output image.

[0007] Preferably, in an optional implementation manner, performing a noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value includes: Performing a wavelet decomposition operation according to the infrared thermal imaging information to obtain a low-frequency coefficient and a first high-frequency coefficient; According to the first high frequency coefficient, a high frequency coefficient threshold processing operation is performed to obtain a second high frequency coefficient; A denoising wavelet coefficient value generation operation is performed according to the second high-frequency coefficient and the low-frequency coefficient to obtain a denoising wavelet coefficient value.

[0008] Preferably, in an optional implementation, a threshold function construction operation is performed according to the denoised wavelet coefficient value to obtain a local weight threshold function and a high threshold function, including: Performing a first local energy model construction operation according to the denoised wavelet coefficient values ​​to obtain a first local energy model; Performing a high threshold function construction operation according to the denoised wavelet coefficient value and the first local energy model to obtain a high threshold function; According to the denoised wavelet coefficient value, a local weight threshold function construction operation is performed to obtain a local weight threshold function.

[0009] Preferably, in an optional implementation manner, the calculation formula for the first local energy model building operation is as follows: The calculation formula for the high threshold function construction operation is as follows: The calculation formula for the local weight threshold function construction operation is as follows: in, represents the first local energy model, Indicates The weight of the first local energy model corresponding to pixels, represents the Gaussian distribution function of the first local energy model, represents the mean value in the Gaussian distribution function of the first local energy model, represents the standard deviation in the Gaussian distribution function of the first local energy model, Represents the denoised wavelet coefficient value; represents the high threshold function, represents the first amplitude parameter, represents the second amplitude parameter, represents the segmentation threshold; represents the local weight threshold function, express pixel values, express pixel value, represents the regularization parameter, Indicates The weight parameter of each pixel, represents the smoothing coefficient, Indicates The denoised wavelet coefficient value corresponding to the pixel, Represents the maximum value of the denoised wavelet coefficients.

[0010] Preferably, in an optional implementation, performing an image reconstruction operation according to the denoised wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data includes: Performing a local weight threshold processing operation according to the denoised wavelet coefficient value and the local weight threshold function to obtain a local weight wavelet coefficient value; Performing a high threshold processing operation according to the denoised wavelet coefficient value and the high threshold function to obtain a high threshold wavelet coefficient value; Performing a first data fusion operation according to the local weighted wavelet coefficient value and the high threshold wavelet coefficient value to obtain a fused wavelet coefficient value; A wavelet reconstruction operation is performed according to the fused wavelet coefficient values ​​to obtain reconstructed image data.

[0011] Preferably, in an optional implementation, performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image includes: According to the reconstructed image data, an operation of obtaining Gaussian distribution parameters is performed to obtain Gaussian distribution parameters; Performing a second local energy model construction operation according to the Gaussian distribution parameters to obtain a second local energy model; performing a curve feature extraction operation according to the second local energy model to obtain curve features; Performing a first image enhancement operation according to the curve feature to obtain a first enhanced image; The calculation formula of the second local energy model building operation is as follows: in, represents the second local energy model, Indicates The weight of the second local energy model corresponding to pixels, represents the second local energy model Gaussian distribution function, represents the mean value in the Gaussian distribution function of the second local energy model, represents the standard deviation in the Gaussian distribution function of the second local energy model, Represents the fused wavelet coefficient value.

[0012] Preferably, in an optional implementation manner, performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image includes: Performing a Laplacian operator processing operation on the first enhanced image to obtain a Laplacian image; Performing an exponential transformation operation according to the Laplace image to obtain an exponential transformation image; Performing a Gaussian distribution feature extraction operation according to the first enhanced image to obtain a Gaussian distribution feature; A second data fusion operation is performed according to the Gaussian distribution characteristics and the exponential transformation image to obtain a second enhanced image.

[0013] In a second aspect, the present invention provides a data processing system for infrared thermal imaging, comprising: A data acquisition module, used to acquire infrared thermal imaging information; A noise reduction module, used for performing noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; A threshold function construction module is used to perform a threshold function construction operation according to the denoising wavelet coefficient value to obtain a local weight threshold function and a high threshold function; An image reconstruction module, used to perform an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; A first image enhancement module, configured to perform a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; A second image enhancement module, configured to perform a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; An image output module is used to perform an image output operation according to the second enhanced image to obtain an output image.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a data processing method for infrared thermal imaging as described above.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned data processing methods for infrared thermal imaging.

[0016] Compared with the prior art, the present invention has the following beneficial effects: by providing a data processing method and system for infrared thermal imaging, it is possible to achieve smoothing of noise while maintaining image details. First, the present invention obtains infrared thermal imaging information and performs noise reduction operations to obtain denoising wavelet coefficient values, thereby effectively reducing the noise components in the image. Then, the denoising wavelet coefficient values ​​are used to construct a local weight threshold function and a high threshold function. The construction of these two functions enables the detailed information and overall structure of the image to be processed more accurately during the image reconstruction process. The reconstructed image data obtained by the image reconstruction operation further reduces the influence of noise while retaining the main features of the image. Then, through two image enhancement operations, the reconstructed image is respectively subjected to local detail enhancement and overall contrast enhancement, so that the detailed information of the image is more prominent, and the overall visual effect is improved. Finally, the output image. It is possible to achieve smoothing of noise while maintaining image details.

[0017] In summary, the present invention provides a data processing method and system for infrared thermal imaging, which aims to solve the problem that it is difficult to effectively suppress noise while enhancing image detail information in the prior art. The method first obtains infrared thermal imaging information and performs a denoising operation on it to obtain a denoised wavelet coefficient value. This step reduces the noise component in the image through wavelet decomposition and high-frequency coefficient threshold processing operations. Then, a local weight threshold function and a high threshold function are constructed according to the denoised wavelet coefficient value. The introduction of these two functions enables the detailed information and overall structure of the image to be processed more accurately in the subsequent image reconstruction process, avoiding the loss of detailed information and excessive introduction of noise. The reconstructed image data obtained by the image reconstruction operation further reduces the influence of noise while retaining the main features of the image. Subsequently, two image enhancement operations are performed. The first enhancement operation performs local detail enhancement on the reconstructed image by constructing a second local energy model and extracting curve features, so that the detailed information of the image is more prominent; the second enhancement operation performs overall contrast enhancement on the first enhanced image by Laplace operator processing and exponential transformation, further improving the overall visual effect of the image. Finally, the output image. It can provide high-quality image data support for applications in fields such as industrial detection, and improve the accuracy and reliability of image analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a data processing method for infrared thermal imaging provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of a data processing system for infrared thermal imaging provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 The first embodiment of the present invention provides a data processing method for infrared thermal imaging, comprising the following steps: S11 obtains infrared thermal imaging information; S12 performs a noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; S13 performs a threshold function construction operation according to the denoised wavelet coefficient value to obtain a local weight threshold function and a high threshold function; S14, performing an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; S15, performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; S16, performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; S17: performing an image output operation according to the second enhanced image to obtain an output image.

[0021] It should be noted that, firstly, by acquiring infrared thermal imaging information, the original data basis is provided for subsequent processing. Then, a denoising operation is performed, and the noise components in the image are effectively removed by wavelet decomposition and high-frequency coefficient threshold processing, and the denoised wavelet coefficient value is obtained, thereby laying the foundation for the clarity and accuracy of the image. Then, a local weight threshold function and a high threshold function are constructed based on the denoised wavelet coefficient value. The precise construction of these two functions enables the detailed information and overall structure of the image to be processed more finely during the image reconstruction process, avoiding the loss of details and the excessive introduction of noise. The reconstructed image data obtained by the image reconstruction operation retains the main features of the image while further reducing the influence of noise, providing high-quality input for subsequent image enhancement. Subsequently, two image enhancement operations are performed. The first enhancement operation enhances the local details of the reconstructed image by constructing the second local energy model and extracting curve features, so that the detailed information of the image is more prominent; the second enhancement operation enhances the overall contrast of the first enhanced image by Laplace operator processing and exponential transformation, further improving the overall visual effect of the image, so that the details and overall structure of the image are improved. Finally, the output image obtained through the image output operation can provide high-quality image data support for applications in fields such as industrial detection, and improve the accuracy and reliability of image analysis.

[0022] In step S11, infrared thermal imaging information is acquired.

[0023] It should be noted that in step S11, obtaining infrared thermal imaging information is the starting step of the entire data processing method, and its purpose is to provide the original data basis for subsequent noise reduction, threshold function construction, image reconstruction, and image enhancement operations. Infrared thermal imaging information refers to the surface temperature distribution data of an object captured by an infrared thermal imaging device. These data are presented in the form of images and can reflect the temperature differences in different areas of the surface of the object. The infrared thermal imaging device includes an infrared detector and a signal processing system. The infrared detector is responsible for receiving the infrared energy radiated from the surface of the object and converting it into an electrical signal. The signal processing system processes and converts the electrical signal to finally generate an infrared thermal imaging image. In the process of obtaining infrared thermal imaging information, it is necessary to ensure the normal working state of the equipment and appropriate environmental conditions to ensure that the acquired image data is accurate and reliable. In industrial detection scenarios, infrared thermal imaging equipment is used to scan power equipment, mechanical devices, etc. to obtain infrared thermal imaging information of their surface temperature distribution, which provides a basis for subsequent fault diagnosis and performance evaluation. In addition, when obtaining infrared thermal imaging information, other sensor data, such as visible light images, vibration signals, etc., can also be combined to perform multimodal data fusion to obtain more comprehensive detection results. Through these methods, high-quality infrared thermal imaging information can be obtained, laying the foundation for subsequent data processing and analysis.

[0024] In step S12, a noise reduction operation is performed according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value, including: Performing a wavelet decomposition operation according to the infrared thermal imaging information to obtain a low-frequency coefficient and a first high-frequency coefficient; According to the first high frequency coefficient, a high frequency coefficient threshold processing operation is performed to obtain a second high frequency coefficient; A denoising wavelet coefficient value generation operation is performed according to the second high-frequency coefficient and the low-frequency coefficient to obtain a denoising wavelet coefficient value.

[0025] It should be noted that in step S12, the noise reduction operation can accurately identify and remove the noise components in the infrared thermal imaging information while retaining the detailed features of the image to the maximum extent. First, the infrared thermal imaging information is decomposed into low-frequency coefficients and high-frequency coefficients using wavelet decomposition technology. Wavelet decomposition is a multi-scale analysis method that can decompose image signals into wavelet coefficients at different scales, wherein the low-frequency coefficients mainly contain the overall structural information of the image, while the high-frequency coefficients contain the edge, texture and other detail information of the image and noise. Through this decomposition, the different frequency components of the image can be separated, providing a basis for subsequent noise suppression and detail retention. Next, the high-frequency coefficient threshold processing operation is performed on the first high-frequency coefficient. This operation requires setting a reasonable threshold value, which is determined based on the statistical characteristics of the image and empirical knowledge. Exemplarily, the high-frequency coefficient threshold value is set to 0.85. Of course, according to different actual applications and user needs, the high-frequency coefficient threshold value is set to 0.6, etc., and the present invention does not limit this. The high-frequency coefficients below the threshold value are considered to be noise components and are set to zero, while the high-frequency coefficients above the threshold value are considered to be important detail information of the image and are retained. In this way, the noise components in the image can be effectively removed while retaining important detail information. Finally, the processed second high-frequency coefficient is combined with the low-frequency coefficient to generate the denoised wavelet coefficient value. This process recombines the denoised high-frequency coefficient with the low-frequency coefficient through the reconstruction algorithm to generate the denoised wavelet coefficient value. The denoised wavelet coefficient value plays a key role in the subsequent image reconstruction and enhancement processing. In industrial equipment detection, the image blur and noise interference caused by environmental interference, equipment noise or sensor error can be removed through the denoising operation, making the key information such as the thermal abnormal area and temperature distribution of the equipment more clearly visible, thereby improving the accuracy and reliability of fault diagnosis.

[0026] In step S13, a threshold function construction operation is performed according to the denoised wavelet coefficient value to obtain a local weight threshold function and a high threshold function, including: Performing a first local energy model construction operation according to the denoised wavelet coefficient values ​​to obtain a first local energy model; Performing a high threshold function construction operation according to the denoised wavelet coefficient value and the first local energy model to obtain a high threshold function; According to the denoised wavelet coefficient value, a local weight threshold function construction operation is performed to obtain a local weight threshold function.

[0027] The calculation formula of the first local energy model building operation is as follows: The calculation formula for the high threshold function construction operation is as follows: The calculation formula for the local weight threshold function construction operation is as follows: in, represents the first local energy model, Indicates The weight of the first local energy model corresponding to pixels, represents the Gaussian distribution function of the first local energy model, represents the mean value in the Gaussian distribution function of the first local energy model, represents the standard deviation in the Gaussian distribution function of the first local energy model, Represents the denoised wavelet coefficient value; represents the high threshold function, represents the first amplitude parameter, represents the second amplitude parameter, represents the segmentation threshold; represents the local weight threshold function, express pixel values, express pixel value, represents the regularization parameter, Indicates The weight parameter of each pixel, represents the smoothing coefficient, Indicates The denoised wavelet coefficient value corresponding to the pixel, Represents the maximum value of the denoised wavelet coefficients.

[0028] It should be noted that in step S13, the threshold function construction operation can provide accurate parameter guidance for image reconstruction to achieve fine processing of image details and overall structure. This operation is achieved by constructing a local weight threshold function and a high threshold function. First, the first local energy model construction operation is performed according to the denoised wavelet coefficient value. The local energy model is a mathematical model that describes the energy distribution of a local area of ​​an image, which can reflect the intensity change of the image in this area. For example, the denoised wavelet coefficient value The distribution in a local area is more concentrated, and the pixel weight in this area is relatively large, then by calculating , we can get a higher local energy value, where So is the mean, is the Gaussian distribution function with standard deviation. This means that this region has relatively important structural features in the image. Next, the high threshold function is constructed using the first local energy model and the denoised wavelet coefficient value. High threshold function By setting a segmentation threshold , the value of the first local energy model is compared with a threshold. Exemplarily, Set to 0.7, the first amplitude parameter Set to 1.2, the second amplitude parameter Set to 0.8, when When it is greater than 0.7, take the first amplitude parameter =1.2, otherwise take the second amplitude parameter =0.8. Of course, depending on the actual application and user needs, Set to 0.6, the first amplitude parameter Set to 1.4, the second amplitude parameter It is set to 0.5, etc., and the present invention does not limit this. For areas with higher local energy, that is, greater than the set segmentation threshold, the high threshold function will give a larger enhancement amplitude, while for areas with lower energy, moderate suppression is performed. Finally, a local weight threshold function construction operation is performed according to the denoising wavelet coefficient value. This function can assign different weights according to the importance of the pixel in the image, thereby achieving protection of detail information and suppression of noise during image reconstruction. When processing infrared thermal imaging images of industrial equipment, by constructing a suitable threshold function, the overheated area of ​​the equipment can be highlighted, while smoothing the surrounding normal temperature area, allowing technicians to perform fault diagnosis and analysis more accurately.

[0029] In step S14, an image reconstruction operation is performed according to the denoised wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data, including: Performing a local weight threshold processing operation according to the denoised wavelet coefficient value and the local weight threshold function to obtain a local weight wavelet coefficient value; Performing a high threshold processing operation according to the denoised wavelet coefficient value and the high threshold function to obtain a high threshold wavelet coefficient value; Performing a first data fusion operation according to the local weighted wavelet coefficient value and the high threshold wavelet coefficient value to obtain a fused wavelet coefficient value; A wavelet reconstruction operation is performed according to the fused wavelet coefficient values ​​to obtain reconstructed image data.

[0030] It should be noted that in step S14, the image reconstruction operation can integrate the information of the denoised wavelet coefficient value, the local weight threshold function and the high threshold function to accurately restore the details and structure of the image. First, the denoised wavelet coefficient value and the local weight threshold function are used to perform a local weight threshold processing operation. In this process, each wavelet coefficient value will be adjusted according to its corresponding local weight threshold function, which takes into account the importance of the pixel point in the image, and by calculating the square of the difference between the pixel value and other pixel values ​​in the neighborhood, and combining the weight parameter, the regularization parameter and the smoothing coefficient, an appropriate weight is assigned to each pixel point. For edge pixels in the image, their weights will be set higher to retain more detail information. Then, a high threshold processing operation is performed according to the denoised wavelet coefficient value and the high threshold function. The high threshold function enhances or suppresses the wavelet coefficient value according to the comparison result between the value of the first local energy model and the segmentation threshold. For areas with higher local energy, such as significant features or target areas in the image, the high threshold function will assign a larger enhancement amplitude to make it more prominent in the reconstructed image. Then, the local weighted wavelet coefficient values ​​and the high threshold wavelet coefficient values ​​are subjected to the first data fusion operation to obtain the fused wavelet coefficient values. This fusion process combines the results of the local weighted threshold processing and the high threshold processing, so that the detail information and the overall structure information of the image are better balanced and coordinated. Finally, a wavelet reconstruction operation is performed according to the fused wavelet coefficient values ​​to obtain the reconstructed image data. Wavelet reconstruction is an inverse operation that recombines the processed wavelet coefficient values ​​to restore the original structure and details of the image. In the processed fused wavelet coefficient values, detail information such as edges and textures is enhanced, while noise and unimportant details are suppressed, thereby obtaining a reconstructed image with high clarity and accurate structure. Through this series of image reconstruction operations, the quality of infrared thermal imaging images can be effectively improved.

[0031] In step S15, a first image enhancement operation is performed according to the reconstructed image data to obtain a first enhanced image, including: According to the reconstructed image data, an operation of obtaining Gaussian distribution parameters is performed to obtain Gaussian distribution parameters; Performing a second local energy model construction operation according to the Gaussian distribution parameters to obtain a second local energy model; performing a curve feature extraction operation according to the second local energy model to obtain curve features; Performing a first image enhancement operation according to the curve feature to obtain a first enhanced image; The calculation formula of the second local energy model building operation is as follows: in, represents the second local energy model, Indicates The weight of the second local energy model corresponding to pixels, represents the second local energy model Gaussian distribution function, represents the mean value in the Gaussian distribution function of the second local energy model, represents the standard deviation in the Gaussian distribution function of the second local energy model, Represents the fused wavelet coefficient value.

[0032] It should be noted that in step S15, image processing technology is used to improve the local details and overall quality of the reconstructed image data. First, Gaussian distribution parameters are extracted from the reconstructed image data, including the mean and standard deviation. These parameters are the key to describing the distribution characteristics of image data in different regions, and they help understand the brightness distribution of the image. The mean represents the average brightness of the image area, while the standard deviation describes the dispersion of the brightness values. Next, these Gaussian distribution parameters are used to construct a second local energy model. This model highlights local changes in the image by calculating the energy value of each pixel, providing a basis for subsequent feature extraction and enhancement. Specifically, using the formula To build the model, It is The weight corresponding to each pixel is So is the mean, is a Gaussian distribution function of the standard deviation. This model can capture subtle changes in the image and provide information for image enhancement. After constructing the second local energy model, a curve feature extraction operation is performed. The purpose of this step is to extract key information such as edges and textures from the image. Curve feature extraction can be achieved by a gradient operator, a Laplace operator, or a model-based fitting technique, which is not limited in the embodiments of the present invention. These techniques can help identify important structures in the image and provide precise guidance for image enhancement. Finally, the first image enhancement operation is performed in combination with the extracted curve features. This operation makes the image detail information more prominent by enhancing the local contrast and clarity of the image. In industrial detection, the enhanced image can clearly show the thermal anomaly area of ​​the equipment and provide visual support for fault diagnosis. The usability and analysis accuracy of infrared thermal imaging images can be improved, so that the final output image is more in line with the needs of practical applications.

[0033] In step S16, performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image includes: Performing a Laplacian operator processing operation on the first enhanced image to obtain a Laplacian image; Performing an exponential transformation operation according to the Laplace image to obtain an exponential transformation image; Performing a Gaussian distribution feature extraction operation according to the first enhanced image to obtain a Gaussian distribution feature; A second data fusion operation is performed according to the Gaussian distribution characteristics and the exponential transformation image to obtain a second enhanced image.

[0034] It should be noted that in step S16, first, the first enhanced image is processed using the Laplace operator to obtain a Laplace image. The Laplace operator is a second-order derivative operator that can highlight rapidly changing areas in the image, such as edges and textures, thereby enhancing the details of these areas. By calculating the Laplace image, high-frequency information in the image can be obtained, which corresponds to significant features in the image. Next, an exponential transformation operation is performed on the Laplace image to obtain an exponential transformation image. The exponential transformation is a nonlinear mapping that can enhance dark areas in an image while suppressing bright areas, which can improve the contrast of the image, especially in low grayscale areas of the image. The specific formula of the exponential transformation can be expressed as ,in represents the pixel value of the original image, represents the coefficients of the exponential transformation, Represents the pixel value after the transformation. After obtaining the exponential transformation image, the Gaussian distribution features are extracted from the first enhanced image again. This step is similar to the Gaussian distribution parameter extraction in step S15, but this time the focus is on the image data after the first enhancement. The extracted Gaussian distribution features, including the mean and standard deviation, can further describe the distribution characteristics of the image data. Finally, the Gaussian distribution features and the exponential transformation image are subjected to a second data fusion operation to obtain a second enhanced image. The data fusion operation combines the statistical information of the Gaussian distribution features and the contrast enhancement effect of the exponential transformation image, so that the overall contrast of the second enhanced image is improved while retaining the image details. This fusion operation can be achieved by weighted averaging, superposition or other more complex fusion algorithms, the purpose of which is to combine the advantages of the two image data to obtain a final high-quality image.

[0035] It is worth noting that in the infrared thermal imaging image of industrial equipment, after the first enhancement operation, although the details of the image are improved, the overall contrast is still insufficient. Through the processing in step S16, the edge and texture information of the image are first extracted using the Laplace operator, and then the dark area of ​​the image is enhanced by exponential transformation, and finally the data is fused in combination with the Gaussian distribution feature. The processed second enhanced image not only retains the detail information of the first enhanced image, but also improves the overall contrast, making the thermal abnormality area of ​​the equipment more obvious.

[0036] In step S17, an image output operation is performed according to the second enhanced image to obtain an output image.

[0037] It should be noted that the final image output operation is performed in step S17, and the processed second enhanced image is converted into the final output image for further analysis or direct application in actual industrial detection tasks. The image output operation ensures the availability and practicality of the image data, and includes steps such as format conversion, image compression, and color adjustment to adapt to different display devices or storage requirements. In this step, first ensure that the color and contrast of the second enhanced image are adjusted to the optimal state so that all important details and features are clearly displayed. This involves color correction of the image to ensure that the temperature information in the image is accurately reflected. In addition, the image needs to be compressed to reduce the storage space occupied or speed up the image transmission speed while ensuring that the image quality is not greatly affected. For example, an infrared thermal imaging image of an electric power equipment is processed. After the second enhancement operation, the image can clearly show the thermal abnormality area of ​​the equipment. In step S17, the color of this image is adjusted to a range suitable for human eye observation, and pseudo-color is used to represent different temperature areas, so that the high temperature area is displayed in red and the low temperature area is displayed in blue. At the same time, the image will be appropriately compressed to facilitate transmission of the image data to a remote monitoring center or storage on a cloud server.

[0038] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0039] In the industrial field, especially in the maintenance of power systems, it is crucial to perform regular thermal imaging inspections on equipment. By analyzing the heat distribution on the surface of the equipment, overheating areas can be discovered in time to prevent potential failures and fire risks. The method of the present invention aims to improve the quality of infrared thermal imaging images, making them more suitable for industrial detection and fault diagnosis.

[0040] Step 1: Obtain infrared thermal imaging information. Use infrared thermal imaging equipment to scan key equipment in the power system, such as transformers and switchgear. The thermal imaging information captured by the equipment is presented in the form of images, showing the temperature differences in different areas, providing the raw data basis for subsequent processing.

[0041] Step 2: Perform noise reduction based on infrared thermal imaging information. The original image is decomposed into low-frequency coefficients and high-frequency coefficients through wavelet decomposition technology. The low-frequency coefficients contain the overall structural information of the image, while the high-frequency coefficients contain details and noise information. Then, threshold processing is applied to the high-frequency coefficients, and the coefficients below the threshold are set to zero to remove noise, while the coefficients above the threshold are retained to maintain image details.

[0042] Step 3: According to the denoised wavelet coefficient value, a threshold function is constructed. A local weight threshold function and a high threshold function are constructed, which will be used in the image reconstruction process to finely process image details and overall structure. The local weight threshold function assigns weights according to the difference between the pixel value and its neighborhood, while the high threshold function enhances or suppresses the wavelet coefficients according to the comparison result between the value of the local energy model and the segmentation threshold.

[0043] Step 4: Perform image reconstruction operation. Reconstruct the image using the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain the reconstructed image data.

[0044] Step 5: Perform the first image enhancement operation based on the reconstructed image data. By constructing the second local energy model and extracting curve features, the local details of the image are enhanced, making the thermal anomaly area more obvious.

[0045] Step 6: Perform a second image enhancement operation based on the first enhanced image. Use Laplace operator processing and exponential transformation to further enhance the overall contrast of the image, so that both the details and the overall structure of the image are improved.

[0046] Step 7: Perform image output operation based on the second enhanced image, adjust the color and contrast to adapt to different display devices or storage requirements, and finally obtain a high-quality output image.

[0047] In summary, the present invention provides an infrared thermal imaging image processing method. First, the temperature distribution data of the device surface is obtained by an infrared thermal imaging device to form an original thermal imaging image. Then, the image is subjected to denoising processing using wavelet decomposition technology to effectively remove noise while retaining key detail information. Next, a local weight threshold function and a high threshold function are constructed. These two functions play a key role in the image reconstruction process, so that the detail information and overall structure of the image are finely processed. The image reconstruction operation combines the information of the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to accurately restore the image details and structure. The subsequent image enhancement operation further enhances the local details and overall contrast of the image, making the thermal anomaly area more obvious. Finally, a high-quality image is obtained through the image output operation. It is achieved that the image details are maintained while the noise is smoothed, providing accurate image data support for applications in fields such as industrial detection, and improving the accuracy and reliability of image analysis.

[0048] Reference Figure 2 The second embodiment of the present invention provides a data processing system for infrared thermal imaging, comprising: A data acquisition module, used to acquire infrared thermal imaging information; A noise reduction module, used for performing noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; A threshold function construction module is used to perform a threshold function construction operation according to the denoising wavelet coefficient value to obtain a local weight threshold function and a high threshold function; An image reconstruction module, used to perform an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; A first image enhancement module, configured to perform a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; A second image enhancement module, configured to perform a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; An image output module is used to perform an image output operation according to the second enhanced image to obtain an output image.

[0049] It should be noted that a data processing system for infrared thermal imaging provided in an embodiment of the present invention is used to execute all process steps of a data processing method for infrared thermal imaging in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0050] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a second image enhancement program. When the processor executes the computer program, the steps in the above-mentioned data processing method for infrared thermal imaging are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the image output module.

[0051] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0052] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0053] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0054] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0055] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0056] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0057] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data processing method for infrared thermal imaging, characterized in that: Executed by a computer, including: Obtain infrared thermal imaging information; Perform a noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; According to the denoising wavelet coefficient value, a threshold function construction operation is performed to obtain a local weight threshold function and a high threshold function; Performing an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; Performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; Performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; An image output operation is performed according to the second enhanced image to obtain an output image.

2. The data processing method for infrared thermal imaging according to claim 1, characterized in that: A denoising operation is performed according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value, including: Performing a wavelet decomposition operation according to the infrared thermal imaging information to obtain a low-frequency coefficient and a first high-frequency coefficient; According to the first high frequency coefficient, a high frequency coefficient threshold processing operation is performed to obtain a second high frequency coefficient; A denoising wavelet coefficient value generation operation is performed according to the second high-frequency coefficient and the low-frequency coefficient to obtain a denoising wavelet coefficient value.

3. The data processing method for infrared thermal imaging according to claim 1, characterized in that: According to the denoised wavelet coefficient value, a threshold function construction operation is performed to obtain a local weight threshold function and a high threshold function, including: Performing a first local energy model construction operation according to the denoised wavelet coefficient values ​​to obtain a first local energy model; Performing a high threshold function construction operation according to the denoised wavelet coefficient value and the first local energy model to obtain a high threshold function; According to the denoised wavelet coefficient value, a local weight threshold function construction operation is performed to obtain a local weight threshold function.

4. The data processing method for infrared thermal imaging according to claim 3, characterized in that: The calculation formula of the first local energy model building operation is as follows: The calculation formula for the high threshold function construction operation is as follows: The calculation formula for the local weight threshold function construction operation is as follows: in, represents the first local energy model, Indicates The weight of the first local energy model corresponding to pixels, represents the Gaussian distribution function of the first local energy model, represents the mean value in the Gaussian distribution function of the first local energy model, represents the standard deviation in the Gaussian distribution function of the first local energy model, Represents the denoised wavelet coefficient value; represents the high threshold function, represents the first amplitude parameter, represents the second amplitude parameter, represents the segmentation threshold; represents the local weight threshold function, express pixel values, express pixel value, represents the regularization parameter, Indicates The weight parameter of each pixel, represents the smoothing coefficient, Indicates The denoised wavelet coefficient value corresponding to the pixel, Represents the maximum value of the denoised wavelet coefficients.

5. The data processing method for infrared thermal imaging according to claim 1, characterized in that: Performing an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data includes: Performing a local weight threshold processing operation according to the denoised wavelet coefficient value and the local weight threshold function to obtain a local weight wavelet coefficient value; According to the denoised wavelet coefficient value and the high threshold function, a high threshold processing operation is performed to obtain a high threshold wavelet coefficient value; Performing a first data fusion operation according to the local weighted wavelet coefficient value and the high threshold wavelet coefficient value to obtain a fused wavelet coefficient value; A wavelet reconstruction operation is performed according to the fused wavelet coefficient values ​​to obtain reconstructed image data.

6. The data processing method for infrared thermal imaging according to claim 1, characterized in that: Performing a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image includes: According to the reconstructed image data, an operation of obtaining Gaussian distribution parameters is performed to obtain Gaussian distribution parameters; Performing a second local energy model construction operation according to the Gaussian distribution parameters to obtain a second local energy model; performing a curve feature extraction operation according to the second local energy model to obtain curve features; Performing a first image enhancement operation according to the curve feature to obtain a first enhanced image; The calculation formula of the second local energy model building operation is as follows: in, represents the second local energy model, Indicates The weight of the second local energy model corresponding to pixels, represents the second local energy model Gaussian distribution function, represents the mean value in the Gaussian distribution function of the second local energy model, represents the standard deviation in the Gaussian distribution function of the second local energy model, Represents the fused wavelet coefficient value.

7. The data processing method for infrared thermal imaging according to claim 1, characterized in that: Performing a second image enhancement operation according to the first enhanced image to obtain a second enhanced image includes: Performing a Laplacian operator processing operation on the first enhanced image to obtain a Laplacian image; Performing an exponential transformation operation according to the Laplace image to obtain an exponential transformation image; Performing a Gaussian distribution feature extraction operation according to the first enhanced image to obtain a Gaussian distribution feature; A second data fusion operation is performed according to the Gaussian distribution characteristics and the exponential transformation image to obtain a second enhanced image.

8. A data processing system for infrared thermal imaging, characterized in that: include: A data acquisition module, used to acquire infrared thermal imaging information; A noise reduction module, used for performing noise reduction operation according to the infrared thermal imaging information to obtain a denoised wavelet coefficient value; A threshold function construction module is used to perform a threshold function construction operation according to the denoising wavelet coefficient value to obtain a local weight threshold function and a high threshold function; An image reconstruction module, used to perform an image reconstruction operation according to the denoising wavelet coefficient value, the local weight threshold function and the high threshold function to obtain reconstructed image data; A first image enhancement module, configured to perform a first image enhancement operation according to the reconstructed image data to obtain a first enhanced image; A second image enhancement module, configured to perform a second image enhancement operation according to the first enhanced image to obtain a second enhanced image; An image output module is used to perform an image output operation according to the second enhanced image to obtain an output image.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a data processing method for infrared thermal imaging as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a data processing method for infrared thermal imaging as described in any one of claims 1 to 7.

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