Image feature enhancement method and device for monitoring equipment

Abstract

The invention discloses an image feature enhancement method and device for monitoring equipment, and relates to the field of digital image processing. Obtaining an original image, preprocessing the original image to obtain a brightness mean value, and averagely dividing the original image to obtain a plurality of image areas; extracting image features of each image area, and adding an image mark for the image area according to the image features; storing the image areas into corresponding mark libraries according to the image marks; image enhancement is carried out on the image areas in the detail mark library and the noise mark library; splicing the enhanced image areas in the coordinate mark library, the detail mark library and the noise mark library according to coordinates to obtain a fused image, and adjusting the brightness of pixel points in the fused image according to a brightness mean value; according to the processing method, different parts of the image can be refined more effectively, the overall quality of the image is improved, and meanwhile, the color accuracy and the detail authenticity are kept.

Description

Technical Field

[0001] The invention belongs to the field of digital image processing, and in particular relates to an image feature enhancement method and device for monitoring equipment. Background Art

[0002] The monitoring system is a comprehensive system that integrates video acquisition, transmission, storage and display technologies, and is used to monitor and record specific areas in real time. It can provide real-time video images, help monitoring personnel to detect abnormal situations in time, and take corresponding measures. It is widely used in public security, traffic management, enterprise security and other fields.

[0003] When shooting images in a low-light environment, due to insufficient light, the images often show the characteristics of low brightness, low contrast and high noise, which makes the details and key information in the image difficult to identify. In order to improve the image quality, the image recognizability is enhanced by analyzing the features in the image, such as color, details and noise. In a low-light environment, color information may be distorted, so color correction is used to restore the true color distribution. At the same time, contrast and brightness enhancement algorithms are used to improve the contrast and brightness of the image so that the details in the dark can be displayed. In addition, detail enhancement technology is also used to highlight the detailed features in the image and enhance the clarity of the image. The application of these technologies can improve the recognition of images shot in a low-light environment and provide a certain reference value for subsequent image analysis.

[0004] Although the above technologies can improve image quality in low-light environments, they also have disadvantages. For example, color correction and enhancement algorithms may not be able to fully restore the true color information, resulting in image color distortion. In addition, contrast and brightness enhancement may introduce noise or over-enhancement, affecting the naturalness and authenticity of the image. Detail enhancement may also cause the image to be over-sharpened and lose details. Excessive unified optimization makes the image unusable. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that excessive unified optimization makes the image useless, and to propose an image feature enhancement method and device for monitoring equipment.

[0006] In a first aspect of the present invention, a method for enhancing image features of a monitoring device is first proposed, the method comprising:

[0007] Optionally, the method comprises:

[0008] Acquire an original image, preprocess the original image to obtain a brightness mean, and evenly divide the original image to obtain multiple image regions;

[0009] Convert the image area from the spatial domain to the frequency domain, calculate the average value of the high-frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds a preset detail value, add a detail mark to the image area;

[0010] Pixels in the image area whose numerical deviation from surrounding pixels exceeds a threshold are obtained as noise points. If the number of noise points exceeds a preset number, a noise point mark is added to the image area.

[0011] Enhance the image regions in the detail mark library and the noise mark library respectively;

[0012] The enhanced image areas in the coordinate mark library, detail mark library and noise mark library are stitched together according to the coordinates to obtain a fused image, and the brightness of the pixels in the fused image is adjusted according to the brightness mean.

[0013] Optionally, obtaining an original image and preprocessing the original image to obtain a brightness mean value includes:

[0014] The first data is obtained by converting the original image from the RGB color space to the XYZ color space, the second data is obtained by converting the first data from the XYZ color space to the Lab color space, and the third data is obtained by binarizing the second data;

[0015] The average brightness of the image in the third data is calculated as the brightness mean.

[0016] Optionally, adding an image tag to the image region according to the image feature includes:

[0017] The image area is converted from the spatial domain to the frequency domain using a frequency domain analysis method to calculate the average value of the high frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds a preset detail value, a detail mark is added to the image area;

[0018] Obtain pixels in the image area whose numerical deviation from surrounding pixels exceeds a threshold, and if the number of such pixels exceeds a preset number, add a noise mark to the image area.

[0019] Optionally, enhancing the image regions in the detail marker library and the noise marker library respectively includes:

[0020] If the detail mark library has detail marks, performing detail enhancement processing on the image area in the area mark library having detail marks by histogram equalization to obtain an enhanced area;

[0021] If the noise mark library has a noise mark, a noise reduction process is performed on the image area in the area mark library having the noise mark by Gaussian filtering to obtain an enhanced area.

[0022] Optionally, adjusting the brightness of pixels in the fused image according to the brightness mean value includes:

[0023] The fused image is converted from RGB color space to Lab color space and the correction parameters of a channel and b channel at pixel point (i, j) are calculated by color correction formula. and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image;

[0024] Adjust any pixel in the restored image by the brightness mean and inversely convert the restored image from the Lab color space to the RGB color space to obtain the final enhanced image;

[0025] The color correction formula:

[0026]

[0027] Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

[0028] In a second aspect of the present invention, an image feature enhancement device for monitoring equipment is provided, comprising:

[0029] Optionally, the device includes a preprocessing module, a label adding module, a label storage module, an image enhancement module, and an image fusion module:

[0030] The preprocessing module is used to obtain an original image, preprocess the original image to obtain a brightness mean, and evenly divide the original image to obtain multiple image regions;

[0031] The tag adding module is used to extract the image features of each image area and add image tags to the image area according to the image features; the image features include the clarity of image details and the number of noise points; the image tags include coordinates, detail tags and noise points tags; if the clarity value of the image area exceeds a preset detail value, a detail tag is added; if the noise points of the image area exceed a threshold, a noise tag is added;

[0032] The tag storage module is used to store each image area into a corresponding tag library according to the image tag; the tag library includes a coordinate tag library, a detail tag library and a noise tag library; the image area in the coordinate tag library only contains coordinates, the image area in the detail tag library contains detail tags, and the image area in the noise tag library contains noise tags;

[0033] The image enhancement module is used to enhance the image areas in the detail mark library and the noise mark library respectively;

[0034] The image fusion module is used to combine the enhanced image areas in the coordinate mark library, detail mark library and noise mark library according to the coordinates to obtain a fused image, and adjust the brightness of the pixels in the fused image according to the brightness mean.

[0035] Optionally, the preprocessing module includes an image conversion module and a brightness calculation module:

[0036] The image conversion module is used to convert the original image from RGB color space to XYZ color space to obtain first data, convert the first data from XYZ color space to Lab color space to obtain second data, and perform binarization processing on the second data to obtain third data;

[0037] The brightness calculation module is used to calculate the average brightness of the images in the third data as the brightness mean.

[0038] Optionally, the marking adding module includes a detail marking module and a noise marking module:

[0039] The detail marking module is used to convert the image area from the spatial domain to the frequency domain, calculate the average value of the high-frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds the preset detail value, add a detail mark to the image area;

[0040] The noise marking module is used to obtain pixel points in the image area whose numerical deviation from surrounding pixel points exceeds a threshold as noise points. If the number of noise points exceeds a preset number, a noise mark is added to the image area.

[0041] Optionally, the image enhancement module includes a detail enhancement module and a noise reduction module:

[0042] The detail enhancement module is used for performing detail enhancement processing on the image area in the area mark library having the detail mark to obtain an enhanced area through histogram equalization if the detail mark library has the detail mark;

[0043] The noise reduction module is used for, if the noise mark library has a noise mark, performing noise reduction processing on the image area in the area mark library having the noise mark to obtain an enhanced area through Gaussian filtering.

[0044] Optionally, the image fusion module includes an image restoration module and a brightness enhancement module:

[0045] The image restoration module is used to convert the fused image from RGB color space to Lab color space and calculate the correction parameters of the a channel and the b channel at the pixel point (i, j) through the color correction formula and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image;

[0046] The brightness enhancement module is used to adjust any pixel in the restored image by the brightness mean and inversely convert the restored image from the Lab color space to the RGB color space to obtain a final enhanced image;

[0047] The color correction formula:

[0048]

[0049]

[0050] Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

[0051] Beneficial effects of the present invention:

[0052] The present invention proposes an image feature enhancement method for monitoring equipment, which obtains an original image, pre-processes the original image to obtain a brightness mean, and evenly divides the original image to obtain multiple image areas; extracts image features of each image area, and adds image tags to the image area according to the image features; stores each image area in a corresponding tag library according to the image tags; enhances the image areas in a detail tag library and a noise tag library respectively; splices the enhanced image areas in a coordinate tag library, a detail tag library, and a noise tag library according to coordinates to obtain a fused image, and adjusts the brightness of pixels in the fused image according to the brightness mean; this processing method can more effectively perform fine processing on different parts of the image, improve the overall quality of the image, and maintain the accuracy of color and the authenticity of details. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 A flowchart of an image feature enhancement method for monitoring equipment is provided for an embodiment of the present invention;

[0055] Figure 2 A flowchart of another method for enhancing image features of a monitoring device is provided for an embodiment of the present invention;

[0056] Figure 3 A structural diagram of an image feature enhancement device for monitoring equipment is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] The embodiment of the present invention provides an image feature enhancement method for monitoring equipment. Figure 1 , Figure 1 A flowchart of an image feature enhancement method for monitoring equipment provided by an embodiment of the present invention. The method comprises the following steps:

[0059] S101, obtaining an original image, preprocessing the original image to obtain a brightness mean, and evenly dividing the original image to obtain multiple image regions.

[0060] S102, extracting image features of each image region, and adding image tags to the image region according to the image features.

[0061] S103, storing each image region into a corresponding label library according to the image label.

[0062] S104, enhancing the image regions in the detail mark library and the noise mark library respectively.

[0063] S105, combining the enhanced image regions in the coordinate mark library, detail mark library and noise mark library according to the coordinates to obtain a fused image, and adjusting the brightness of the pixels in the fused image according to the brightness mean.

[0064] Among them, image features include the clarity of image details and the number of noise points; image tags include coordinates, detail tags and noise tags. If the clarity of the image area exceeds the preset detail value, a detail tag is added; if the noise point of the image area exceeds the threshold, a noise tag is added.

[0065] The mark library includes a coordinate mark library, a detail mark library and a noise mark library; the image area in the coordinate mark library only contains coordinates, the image area in the detail mark library contains detail marks, and the image area in the noise mark library contains noise marks.

[0066] An image feature enhancement method for monitoring equipment provided based on an embodiment of the present invention divides the image into multiple regions, independently optimizes the details and noise of each region, and finally splices and adjusts the overall brightness and color. This processing method can more effectively perform fine processing on different parts of the image, improve the overall quality of the image, and maintain the accuracy of color and the authenticity of details.

[0067] In one implementation, evenly dividing the original image into multiple regions according to the size of the image and optimizing these regions individually is beneficial to improving the efficiency and accuracy of image processing, wherein coordinates are added to the image regions when the original image is divided, and the coordinates contain the position information of the image regions in the original image.

[0068] In one implementation, by adding a mark to the image area, the mark of the image area contains at least one coordinate, or contains two marks at the same time including the coordinate and one of the noise mark and the detail mark, or has three marks at the same time including the coordinate, the noise mark and the detail mark, which is beneficial to improving the accuracy and efficiency of image classification and recognition tasks.

[0069] In one implementation, the image is added to the corresponding coordinate mark library, detail mark library and noise mark library according to the coordinates, detail mark and noise mark of the image area, and the image area in the area mark library is optimized by the corresponding mark concentration, so as to improve the optimization efficiency. For example, in the divided image area, the image area with only coordinates is added to the coordinate mark library, the image with coordinates and noise marks is added to the noise mark library, the image with coordinates and detail marks is added to the detail mark library, and the image with coordinates, noise marks and detail marks is added to the noise mark library and detail mark library respectively; wherein, the coordinate mark cannot be modified and the image area with only coordinates does not need to be enhanced.

[0070] In one implementation, the image area with three marks after enhancement in the mark library is superimposed and optimized, retaining the advantages of the detail mark library and the noise mark library after enhancement, and the complete image is obtained by stitching through the image's own coordinates. Retaining the advantages of the enhanced image facilitates subsequent color correction and adjustment of image brightness.

[0071] In one embodiment, step S101 includes:

[0072] The first data is obtained by converting the original image from the RGB color space to the XYZ color space, the second data is obtained by converting the first data from the XYZ color space to the Lab color space, and the third data is obtained by binarizing the second data;

[0073] The average brightness of the image in the third data is calculated as the brightness mean.

[0074] In one implementation, by converting the original image from RGB color space to XYZ color space and then to Lab color space, it is helpful to more accurately represent and process the brightness channel of the image.

[0075] In one implementation, converting the image to the Lab color space can effectively preserve a wider color gamut.

[0076] In one embodiment, see Figure 2 ,exist Figure 1 On the basis of this, step S102 further includes:

[0077] S1021, converting the image region from the spatial domain to the frequency domain, calculating the average value of the high frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds the preset detail value, adding a detail mark to the image region;

[0078] S1022, obtaining pixels in the image area whose numerical deviation from surrounding pixels exceeds a threshold as noise points, and if the number of noise points exceeds a preset number, adding noise point marks to the image area.

[0079] In one implementation, the clarity is calculated by a frequency domain analysis method and detail marks are added to image areas exceeding a preset value, which is beneficial to improving the accuracy and visualization of image detail information.

[0080] In one implementation, by extracting noise features and adding noise marks to image areas where the noise exceeds a threshold, the efficiency and accuracy of image denoising processing are improved.

[0081] In one embodiment, step S104 further includes:

[0082] If the detail mark library has detail marks, a detail enhancement process is performed on the image region in the region mark library having the detail marks by histogram equalization to obtain an enhanced region.

[0083] If the noise mark library has a noise mark, the image area in the area mark library with the noise mark is subjected to noise reduction processing by Gaussian filtering to obtain an enhanced area.

[0084] In one implementation, histogram equalization can be used to enhance the contrast and details of an image, making the image clearer and fuller, thereby improving the visual effect.

[0085] In one implementation, Gaussian filtering can be used to effectively reduce noise in an image, preserve image details, reduce image blur, and improve image quality.

[0086] In one embodiment, step S105 may include the following steps:

[0087] Convert the fused image from RGB color space to Lab color space and calculate the correction parameters of a channel and b channel at pixel point (i, j) through the color correction formula and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image;

[0088] The brightness mean is adjusted for any pixel in the restored image and the restored image is inversely converted from the Lab color space to the RGB color space to obtain the final enhanced image.

[0089] Color Correction Formula:

[0090]

[0091] Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

[0092] In one implementation, any pixel in the stitched image is processed by the formula and Performing color correction can improve the color accuracy and consistency of your images.

[0093] In one implementation, the brightness of the restored image is corrected by brightness mean value, and the brightness exceeding a preset threshold and the brightness below a preset threshold are corrected to make the brightness of the image more balanced.

[0094] Based on the same inventive concept, the present invention also provides an image feature enhancement device for monitoring equipment. Figure 3 , Figure 3 A framework diagram of an image feature enhancement device for monitoring equipment provided by an embodiment of the present invention includes:

[0095] A preprocessing module is used to obtain an original image, preprocess the original image to obtain a brightness mean, and evenly divide the original image to obtain multiple image regions;

[0096] A tag adding module is used to extract image features of each image area and add image tags to the image area according to the image features; the image features include the clarity of image details and the number of noise points; the image tags include coordinates, detail tags and noise points tags; if the clarity of the image area exceeds a preset detail value, a detail tag is added; if the noise points of the image area exceed a threshold, a noise tag is added;

[0097] A tag storage module is used to store each image area into a corresponding tag library according to the image tag; the tag library includes a coordinate tag library, a detail tag library and a noise tag library; the image area in the coordinate tag library only contains coordinates, the image area in the detail tag library contains detail tags, and the image area in the noise tag library contains noise tags;

[0098] An image enhancement module, used for enhancing the image regions in the detail mark library and the noise mark library respectively;

[0099] The image fusion module is used to combine the enhanced image areas in the coordinate mark library, detail mark library and noise mark library according to the coordinates to obtain a fused image, and adjust the brightness of the pixels in the fused image according to the brightness mean.

[0100] An image feature enhancement device for monitoring equipment provided based on an embodiment of the present invention divides the image into multiple areas, independently optimizes the details and noise of each area, and finally splices and adjusts the overall brightness and color. This processing method can more effectively perform fine processing on different parts of the image, improve the overall quality of the image, and maintain the accuracy of color and the authenticity of details.

[0101] In one embodiment, the preprocessing module includes an image conversion module and a brightness calculation module:

[0102] An image conversion module, used for converting the original image from RGB color space to XYZ color space to obtain first data, converting the first data from XYZ color space to Lab color space to obtain second data, and binarizing the second data to obtain third data;

[0103] The brightness calculation module is used to calculate the average brightness of the image in the third data as the brightness mean.

[0104] In one embodiment, the marking module includes a detail marking module and a noise marking module:

[0105] A detail marking module is used to convert the image area from the spatial domain to the frequency domain, calculate the average value of the high-frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds the preset detail value, add a detail mark to the image area;

[0106] The noise marking module is used to obtain the pixel points in the image area whose numerical deviation from the surrounding pixel points exceeds a threshold as noise points. If the number of noise points exceeds a preset number, a noise mark is added to the image area.

[0107] In one embodiment, the image enhancement module includes a detail enhancement module and a noise reduction module:

[0108] A detail enhancement module, used for performing detail enhancement processing on the image area in the area mark library with the detail mark to obtain an enhanced area through histogram equalization if the detail mark library has the detail mark;

[0109] The denoising module is used for, if the noise mark library has a noise mark, performing denoising processing on the image area in the area mark library having the noise mark by Gaussian filtering to obtain an enhanced area.

[0110] In one embodiment, the image fusion module includes an image restoration module and a brightness enhancement module:

[0111] The image restoration module is used to convert the fused image from RGB color space to Lab color space and calculate the correction parameters of the a channel and b channel at the pixel point (i, j) through the color correction formula and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image;

[0112] The brightness enhancement module is used to adjust the brightness mean of any pixel in the restored image and inversely convert the restored image from the Lab color space to the RGB color space to obtain the final enhanced image;

[0113] Color Correction Formula:

[0114]

[0115] Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

[0116] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for enhancing image features of a monitoring device, characterized in that: The method comprises: Acquire an original image, preprocess the original image to obtain a brightness mean, and evenly divide the original image to obtain multiple image regions; Extracting image features of each image region, and adding image tags to the image region according to the image features; Image features include the clarity of image details and the number of noise points; image tags include coordinates, detail tags and noise tags. If the clarity of the image area exceeds the preset detail value, a detail tag is added; if the noise point of the image area exceeds the threshold, a noise tag is added; According to the image mark, each image area is stored in a corresponding mark library; the mark library includes a coordinate mark library, a detail mark library and a noise mark library; the image area in the coordinate mark library only contains coordinates, the image area in the detail mark library contains detail marks, and the image area in the noise mark library contains noise marks; Enhance the image regions in the detail mark library and the noise mark library respectively; The enhanced image areas in the coordinate mark library, detail mark library and noise mark library are stitched together according to the coordinates to obtain a fused image, and the brightness of the pixels in the fused image is adjusted according to the brightness mean.

2. The image feature enhancement method for monitoring equipment according to claim 1, characterized in that: Get the original image, preprocess the original image to get the brightness mean including: The first data is obtained by converting the original image from the RGB color space to the XYZ color space, the second data is obtained by converting the first data from the XYZ color space to the Lab color space, and the third data is obtained by binarizing the second data; The average brightness of the image in the third data is calculated as the brightness mean.

3. The image feature enhancement method for monitoring equipment according to claim 1, characterized in that: Adding image tags to the image region according to image features includes: Convert the image area from the spatial domain to the frequency domain, calculate the average value of the high-frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds a preset detail value, add a detail mark to the image area; Pixels in the image area whose numerical deviation from surrounding pixels exceeds a threshold are obtained as noise points. If the number of noise points exceeds a preset number, a noise point mark is added to the image area.

4. The image feature enhancement method for monitoring equipment according to claim 1, characterized in that: The image regions in the detail marker library and the noise marker library are enhanced respectively, including: If the detail mark library has detail marks, performing detail enhancement processing on the image area in the area mark library having detail marks by histogram equalization to obtain an enhanced area; If the noise mark library has a noise mark, a noise reduction process is performed on the image area in the area mark library having the noise mark by Gaussian filtering to obtain an enhanced area.

5. The image feature enhancement method for monitoring equipment according to claim 1, characterized in that: The method for adjusting the brightness of pixels in the fused image according to the brightness mean value comprises: The fused image is converted from RGB color space to Lab color space and the correction parameters of a channel and b channel at pixel point (i, j) are calculated by color correction formula. and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image; Adjust any pixel in the restored image by the brightness mean and inversely convert the restored image from the Lab color space to the RGB color space to obtain the final enhanced image; The color correction formula: Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

6. An image feature enhancement device for monitoring equipment, characterized in that: The device includes a preprocessing module, a mark adding module, a mark storage module, an image enhancement module, and an image fusion module: The preprocessing module is used to obtain an original image, preprocess the original image to obtain a brightness mean, and evenly divide the original image to obtain multiple image regions; The tag adding module is used to extract the image features of each image area and add an image tag to the image area according to the image features; Image features include the clarity of image details and the number of noise points; image tags include coordinates, detail tags and noise tags. If the clarity of the image area exceeds the preset detail value, a detail tag is added; if the noise point of the image area exceeds the threshold, a noise tag is added; The tag storage module is used to store each image area into a corresponding tag library according to the image tag; the tag library includes a coordinate tag library, a detail tag library and a noise tag library; the image area in the coordinate tag library only contains coordinates, the image area in the detail tag library contains detail tags, and the image area in the noise tag library contains noise tags; The image enhancement module is used to enhance the image areas in the detail mark library and the noise mark library respectively; The image fusion module is used to combine the enhanced image areas in the coordinate mark library, detail mark library and noise mark library according to the coordinates to obtain a fused image, and adjust the brightness of the pixels in the fused image according to the brightness mean.

7. The image feature enhancement device for monitoring equipment according to claim 6, characterized in that: The preprocessing module includes an image conversion module and a brightness calculation module: The image conversion module is used to convert the original image from RGB color space to XYZ color space to obtain first data, convert the first data from XYZ color space to Lab color space to obtain second data, and perform binarization processing on the second data to obtain third data; The brightness calculation module is used to calculate the average brightness of the images in the third data as the brightness mean.

8. The image feature enhancement device for monitoring equipment according to claim 6, characterized in that: The marking module includes a detail marking module and a noise marking module: The detail marking module is used to convert the image area from the spatial domain to the frequency domain, calculate the average value of the high-frequency part in the frequency domain to obtain the clarity, and if the clarity exceeds the preset detail value, add a detail mark to the image area; The noise marking module is used to obtain pixel points in the image area whose numerical deviation from surrounding pixel points exceeds a threshold as noise points. If the number of noise points exceeds a preset number, a noise mark is added to the image area.

9. The image feature enhancement device for monitoring equipment according to claim 6, characterized in that: The image enhancement module includes a detail enhancement module and a noise reduction module: The detail enhancement module is used for performing detail enhancement processing on the image area in the area mark library having the detail mark to obtain an enhanced area through histogram equalization if the detail mark library has the detail mark; The noise reduction module is used for, if the noise mark library has a noise mark, performing noise reduction processing on the image area in the area mark library having the noise mark to obtain an enhanced area through Gaussian filtering.

10. The image feature enhancement device for monitoring equipment according to claim 6, characterized in that: The image fusion module includes an image restoration module and a brightness enhancement module: The image restoration module is used to convert the fused image from RGB color space to Lab color space and calculate the correction parameters of the a channel and the b channel at the pixel point (i, j) through the color correction formula and pass and Correct the color of the pixel point (i, j) in the a channel and the b channel to obtain a restored image; The brightness enhancement module is used to adjust any pixel in the restored image by the brightness mean and inversely convert the restored image from the Lab color space to the RGB color space to obtain a final enhanced image; The color correction formula: Where M*N is the image size, D a (i,j) and D b (i,j) are the pixels at (i,j) in the a channel and the b channel respectively.

Images