Image processing system based on artificial intelligence

Abstract

The invention discloses an image processing system based on artificial intelligence, and relates to the technical field of image processing, a user inputs an original image into the system by using an image input module, the system receives original image data and displays the image by using a display module, and the image processing module adjusts the original image based on the original image data by using an image adjustment processing module. After brightness and contrast adjustment, noise suppression, edge enhancement and histogram equalization are carried out, the quality of an image displayed on a display module is gradually improved, the defects of a traditional method are overcome, a more intelligent, efficient and high-quality image processing effect is achieved, and particularly, when complex and changeable images are processed, the image quality is improved. The system can automatically analyze the image content and dynamically adjust the processing parameters so as to achieve the optimal processing effect, the image processing system based on artificial intelligence brings a new thought and method for the image processing field, and the system has wide application prospects and important research value.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image processing system based on artificial intelligence. Background Art

[0002] Computer vision, as an important branch of artificial intelligence, aims to enable computer systems to simulate the human visual system so as to understand and process image and video data. With the continuous development of computer technology, computer vision has gradually merged with image processing technology to form an image processing system based on artificial intelligence. This system can automatically analyze, understand and process image data, providing strong support for various application scenarios.

[0003] Traditional image processing algorithms usually use fixed parameter values ​​and cannot be adaptively adjusted according to the specific content of the image, resulting in unsatisfactory processing effects. In terms of noise suppression and edge enhancement, traditional methods often find it difficult to strike a balance between the two. Either excessive smoothing leads to blurred edges, or noise suppression is insufficient. In addition, in traditional image processing, especially when processing images with extreme brightness and contrast ranges, histogram equalization can improve image contrast, but it can lead to loss of image details and excessive enhancement. Finally, traditional methods lack a feedback mechanism during the processing process and cannot dynamically adjust processing parameters according to the processing results to achieve the best processing effect. Summary of the invention

[0004] The purpose of the present invention is to provide an image processing system based on artificial intelligence, which solves the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, and the specific implementation steps are as follows: The user uses the image input module to input the original image into the system, and the system receives the original image data and uses the display module to display the image; Based on the original image data, and using the image adjustment processing module, brightness and contrast adjustment, noise suppression and edge enhancement and histogram equalization are performed to gradually improve the image quality displayed on the display module; Among them, a 3×3 pixel window is selected for local processing of noise suppression and edge enhancement; The image adjustment processing module includes an image visual effect improvement unit, a noise and edge enhancement unit, and an adaptive histogram equalization and feedback adjustment unit; The device used by the image input module includes a computing device; The equipment used by the display module includes a display device; The equipment used by the image adjustment processing module includes an input device; The computing device includes a CPU, which is used to perform basic mathematical operations and logical judgments. The display device includes a display, which is used to display original images and processed images. The input device includes a keyboard, a mouse and a touch screen, and the input device is used for users to input parameters, select processing options and interact with the system.

[0006] Optionally, the calculation formula of the image visual effect improvement unit is as follows: LDT=a×YST+b; in: LDT is the pixel value after enhancement adjustment; a is the contrast adjustment factor; YST is the original image data value, and YST reflects any channel of the gray value matrix and the RGB color matrix; b is the brightness adjustment value; The specific calculation instructions for a and b are as follows: If a>1, the image contrast is enhanced; If a<1, the image contrast is weakened; If b is a positive value, the image brightness is enhanced; If b is a positive value, the image brightness decreases.

[0007] Optionally, the calculation formula of the noise and edge enhancement unit is as follows: ; LDT c，avg =(LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） ) / 9; in: LDT c（x，y） Enhance the pixel value for the (x, y) position within the window; CK （x，y） is the pixel window centered at (x, y) position, CK （x，y） Reflects a 3×3 pixel window with (x,y) as the center coordinate axis; LDT （n，m） is the pixel value at position (n, m) in the window; LDT c，avg is the average value of pixels at position (n, m) in the window; d is the edge enhancement coefficient, which is used to adjust the strength of edge enhancement; LDT （x，y） Enhance pixel value for (x, y) position, LDT （x，y） Reflects the enhanced adjusted pixel value LDT at the position (x, y) in the overall image; The average value of the pixel at position (n, m) in the window LDT c，avg The specific calculation instructions are as follows: Since the center (x, y) position of the pixel window CK （x，y） Reflects a 3×3 pixel window with (x, y) as the center coordinate axis, so the pixel value at position (n, m) in the 3×3 pixel window is LDT （n，m） The total amount of calculation is 9, "LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） ” is the pixel value LDT at position (n, m) in the 9 windows （n，m） The sum of .

[0008] Optionally, the center (x, y) position pixel window CK （x，y） The specific instructions using a 3×3 pixel window are as follows; The 3×3 pixel window size is moderate, which can capture the neighborhood information for analysis and processing, and avoid blurring the image details due to a large window; The 3×3 pixel window has simple geometry and symmetry, which makes it easy to implement and optimize in image processing algorithms; In image processing tasks, 3×3 pixel windows are well represented and recognized within a local neighborhood.

[0009] Optionally, the calculation formula of the adaptive histogram equalization and feedback adjustment unit is as follows: CK （x，y） =ZTH(LDT c（x，y） )×ZSY; ZSY=[1+(LDT max -LDT min ) / (LDT （x，y） -LDT min )]; in: CK （x，y） is the pixel value at the (x, y) position after processing; ZTH is a histogram equalization function, which is used to improve the contrast of the image; ZSY is the adaptive adjustment factor; LDT min The minimum pixel value after enhancement adjustment; LDT max The maximum pixel value after enhancement adjustment.

[0010] Optionally, based on the pixel value CK at the (x, y) position after the processing （x，y）In image processing, it is the final adjustment of the visual effect after processing, and it is adjusted according to the dynamic range standards of different images. Taking an 8-bit image as an example, its dynamic range of brightness is 0 to 255, so 0-255 is the reference range of the width and narrowness of the image brightness; The formula reflecting the brightness width based on the image visual effect improvement unit and the adaptive histogram equalization and feedback adjustment unit is as follows: LF=LDT max -LDT min ; LF is the brightness width value.

[0011] Optionally, the brightness width value LF and the image brightness width reference value 255 are further analyzed and adjusted by image processing as follows: S1. If the result value of the brightness width value LF is below 150 in the range of 0-255, it indicates that the brightness range of the image is narrow and the contrast of the image is low. The adaptive adjustment factor ZSY of the adaptive histogram equalization and feedback adjustment unit should be adjusted and applied to the adaptive histogram equalization and feedback adjustment unit for calculation. The adjustment formula of the adaptive adjustment factor ZSY is as follows: ZSY=[1+(c×LDT max -LDT min ) / (LDT （x，y） -LDT min )]; c is a scaling factor, which is used to enhance the visual effect of histogram equalization and the contrast of the image; S2. If the result value of the brightness width value LF is above 150 and below 255 in the range of 0-255, it indicates that the brightness range of the image is wide. When the current adaptive adjustment factor ZSY is maintained, the pixel value CK at the (x, y) position is output. （x，y） .

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The system of the present invention uses an artificial intelligence algorithm to automatically analyze image content and dynamically adjusts the parameters in the image adjustment processing module, including the image visual effect improvement unit and the noise and edge enhancement unit, according to image features to achieve optimal brightness and contrast adjustment, noise suppression and edge enhancement effects.

[0013] 2. The present invention calculates the local mean and variance in the noise and edge enhancement unit, so that the system can intelligently distinguish between noise and edge information, thereby retaining or even enhancing the edge details of the image while suppressing noise.

[0014] 3. Based on histogram equalization, the present invention introduces a feedback adjustment mechanism to dynamically adjust the brightness and contrast of the equalized image according to the pixel value range of the enhanced adjusted pixel value LDT to avoid the problem of over-enhancement or under-enhancement.

[0015] 4. The system of the present invention organically combines the steps of brightness and contrast adjustment, noise suppression and edge enhancement, adaptive histogram equalization and feedback adjustment to form an efficient and complete image processing flow, thereby significantly improving the quality and efficiency of image processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of the method of the image processing system based on artificial intelligence; Figure 2 It is a structural schematic diagram of the image adjustment processing module of the present invention.

[0017] Figure 3 A schematic diagram of brightness contrast adjustment of the image vision improvement unit of the present invention; Figure 4 Schematic diagram of the 3×3 pixel window benefit of the noise and edge enhancement unit of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] Regarding this artificial intelligence-based image processing system, it is different from traditional image processing methods. Traditional image processing methods often rely on fixed algorithms and parameter settings to process images. These methods often seem to be powerless when processing complex and changeable images, especially in terms of brightness adjustment, contrast enhancement, noise suppression and edge enhancement. Traditional methods are difficult to meet the requirements of high efficiency and high quality at the same time. In addition, histogram equalization, as a common technology for improving image contrast, also has problems of over-enhancement and under-enhancement. This algorithm unit overcomes the shortcomings of traditional methods and achieves more intelligent, efficient and high-quality image processing effects. Especially when processing complex and changeable images, the system can automatically analyze image content and dynamically adjust processing parameters to achieve the best processing effect. This artificial intelligence-based image processing system has brought new ideas and methods to the field of image processing, and has broad application prospects and important research value.

[0020] For example, see Figures 1 to 4, this implementation provides an image processing system based on artificial intelligence, and the specific implementation steps are as follows: The user uses the image input module to input the original image into the system, and the system receives the original image data and uses the display module to display the image; Based on the original image data, the image adjustment processing module is used to adjust brightness and contrast, suppress noise and enhance edges, and perform histogram equalization, thereby gradually improving the image quality displayed on the display module. Among them, a 3×3 pixel window is selected for local processing of noise suppression and edge enhancement; The image adjustment processing module includes an image visual effect improvement unit, a noise and edge enhancement unit, and an adaptive histogram equalization and feedback adjustment unit; The devices used by the image input module include computing devices; The devices used in the display module include display devices; The devices used by the image adjustment processing module include input devices; The computing device includes a CPU, which is used to perform basic mathematical operations and logical judgments. The display device includes a monitor, which is used to display original images and processed images. The input device includes a keyboard, a mouse and a touch screen, and the input device is used by users to input parameters, select processing options and interact with the system.

[0021] In this embodiment, the system combines LDT, LDT c（x，y） and CK （x，y） The three operation results are: LDT is the enhanced adjusted pixel value, which improves the visual effect of the image by adjusting the brightness and contrast of the image, making the image clearer and easier to observe. c（x，y） Enhance the pixel value at the (x, y) position in the window, suppress the noise in the image while maintaining the edge information of the image, and enhance the edge by calculating the difference in pixel values ​​within the local window, while using mean filtering to smooth the noise, CK （x，y） For the pixel value at the (x, y) position after processing, on the basis of histogram equalization, the brightness and contrast of the image are further adjusted by adaptive adjustment factors to achieve more precise control, and combined with the processing results of the first two steps, the contrast and brightness of the image are optimized by histogram equalization and adaptive adjustment, making the image more suitable for subsequent processing and observation, and the calculation results of and can also affect the feedback to LDT and LDT c（x，y） The calculation makes the three algorithms of this system have a high correlation and entanglement, so that the overall algorithm system can perform automatic feedback and optimization according to the actual situation to be closer to reality.

[0022] See also Figures 1 to 4, the calculation formula of the image visual effect improvement unit is as follows: LDT=a×YST+b; in: LDT is the pixel value after enhancement adjustment; a is the contrast adjustment factor; YST is the original image data value, and YST reflects any channel of the gray value matrix and the RGB color matrix; b is the brightness adjustment value; The specific calculation instructions for a and b are as follows: If a>1, the image contrast is enhanced; If a<1, the image contrast is weakened; If b is a positive value, the image brightness is enhanced; If b is a positive value, the image brightness decreases.

[0023] In this embodiment: first, the algorithm unit adjusts the contrast adjustment factor a so that the system can intelligently identify the dark area and the bright area in the image and enhance them respectively. When a>1, the pixel value of the dark area is further reduced, and the pixel value of the bright area is further increased, thereby forming a stronger contrast effect visually. This contrast enhancement helps to highlight the details and layering in the image, making the image more vivid and realistic. The adjustment of the brightness adjustment value b directly affects the brightness level of the entire image. By appropriately increasing or decreasing the brightness adjustment value b, it can ensure that the image is neither overexposed nor overly dark when displayed, thereby retaining more detail information. This fine-tuning of the brightness is crucial to improving the visual comfort of the image. In this embodiment, images taken in an environment with insufficient light or too strong light often have problems of uneven brightness and insufficient contrast. The application of the image visual effect improvement unit can automatically compensate for the adverse effects of these lighting conditions, so that the image quality is significantly improved. Different devices have performance differences in the image acquisition and transmission process, resulting in uneven image quality. Through the adjustment of the image visual effect improvement unit, the image quality problem caused by insufficient device performance can be compensated to a certain extent. In this embodiment, as the first step of the image processing flow, the image visual effect improvement unit provides more ideal input data for the subsequent noise suppression and edge enhancement processing steps by adjusting the brightness and contrast. This optimization helps to improve the efficiency and effectiveness of the subsequent processing steps. By improving the initial quality of the image, the image visual effect improvement unit reduces the difficulty and complexity of the subsequent processing steps. For example, in the noise suppression process, due to the improvement of image brightness and contrast, the system can more easily distinguish between noise and useful information. In the edge enhancement process, due to the enhancement of image layering, the system can more accurately identify edge information.

[0024] See also Figures 1 to 4 , the calculation formula of noise and edge enhancement unit is as follows: ; LDT c，avg =(LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） ) / 9; in: LDT c（x，y） Enhance the pixel value for the (x, y) position within the window; CK （x，y） is the pixel window centered at (x, y) position, CK （x，y） Reflects a 3×3 pixel window with (x,y) as the center coordinate axis; LDT （n，m） is the pixel value at position (n, m) in the window; LDT c，avg is the average value of pixels at position (n, m) in the window; d is the edge enhancement coefficient, which is used to adjust the strength of edge enhancement; LDT （x，y） Enhance pixel value for (x, y) position, LDT （x，y） Reflects the enhanced adjusted pixel value LDT at the position (x, y) in the overall image; The average value of the pixel at position (n, m) in the window LDT c，avg The specific calculation instructions are as follows: Since the center (x, y) position of the pixel window CK （x，y） Reflects a 3×3 pixel window with (x, y) as the center coordinate axis, so the pixel value at position (n, m) in the 3×3 pixel window is LDT （n，m） The total amount of calculation is 9, "LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） ” is the pixel value LDT at position (n, m) in the 9 windows （n，m） The sum of .

[0025] In this embodiment, the noise and edge enhancement unit identifies noise by calculating the difference in pixel values ​​within a local window. This method utilizes the principle of spatial correlation of pixel values ​​in an image, that is, adjacent pixels usually have similar values. When the difference in pixel values ​​within a local window exceeds a certain threshold, the system regards it as noise and suppresses it. While suppressing the noise, the noise and edge enhancement unit also retains the detail information in the image by adjusting the edge enhancement coefficient d. This balanced processing helps to prevent the image from becoming blurred and distorted during the noise suppression process. Edges are the dividing lines between different areas in an image and are also an important basis for image analysis and recognition. The noise and edge enhancement unit makes the edges in the image clearer and more prominent by enhancing edge information, which helps the subsequent edge detection and image segmentation processing steps to be more accurate and efficient. The enhancement of edge information not only improves the visibility of the image, but also enhances the readability of the image. In the fields of medical image analysis and remote sensing image interpretation, the clear presentation of edge information is of great significance to the judgment and decision-making of professionals; The combination of noise suppression and edge enhancement makes the processed image clearer and sharper while maintaining the original details. This combined effect helps to improve the overall image quality and enhance the user experience. Clear images help reduce visual fatigue when people watch for a long time. In the fields of digital entertainment, online education, etc., this improvement in clarity is of great significance to improving user viewing experience and satisfaction.

[0026] See also Figures 1 to 4 , the calculation formula of the adaptive histogram equalization and feedback adjustment unit is as follows: CK （x，y） =ZTH(LDT c（x，y） )×ZSY; ZSY=[1+(LDT max -LDT min ) / (LDT （x，y） -LDT min )]; in: CK （x，y） is the pixel value at the (x, y) position after processing; ZTH is a histogram equalization function, which is used to improve the contrast of the image; ZSY is the adaptive adjustment factor; LDT min The minimum pixel value after enhancement adjustment; LDT max The maximum pixel value after enhancement adjustment.

[0027] In this embodiment, first, the histogram equalization function ZTH is a function used to equalize the histogram of an image. The purpose of the histogram equalization is to improve the contrast of the image and make the histogram distribution of the image more uniform. Specifically, it remaps the pixel values ​​of the image so that the frequency of each pixel value appearing in the image, that is, the height of the histogram, is roughly equal. In this way, the pixel values ​​originally unevenly distributed on the histogram will be stretched to the entire brightness range, thereby enhancing the contrast of the image; In this algorithm unit, histogram equalization is an effective method to enhance image contrast by adjusting the image histogram distribution. The histogram equalization function ZTH in the adaptive histogram equalization and feedback adjustment unit realizes this function. The image contrast is enhanced by stretching the image histogram distribution range. This processing makes the transition between the dark and bright areas in the image smoother and more natural. While enhancing the contrast, the adaptive histogram equalization and feedback adjustment unit also focuses on retaining the detail information in the image. Through reasonable histogram equalization processing parameter settings and the introduction of the adaptive adjustment factor ZSY, the system can avoid the loss and distortion of image details while enhancing the contrast.

[0028] The adaptive adjustment factor ZSY in the adaptive histogram equalization and feedback adjustment unit dynamically adjusts the result after histogram equalization according to the pixel value range of the enhanced adjusted pixel value LDT. This adjustment method enables the system to flexibly process according to the actual conditions of different images, thereby avoiding over-enhancement or under-enhancement. The introduction of the adaptive adjustment factor ZSY enables the adaptive histogram equalization and feedback adjustment unit to achieve more stable and consistent results when processing different images. This optimization processing effect helps to improve the performance and reliability of the entire image processing system. The cyclic influence of the adaptive histogram equalization and feedback adjustment unit on the image visual effect improvement unit is mainly reflected in the feedback mechanism. When the pixel value CK at the (x, y) position after processing is （x，y） When the quality is improved, it can be used as one of the input data of the subsequent image processing process. This feedback mechanism enables the entire image processing system to form a closed-loop control system, which improves the efficiency and effect of image processing through continuous optimization and improvement. Since the adaptive adjustment factor ZSY in the adaptive histogram equalization and feedback adjustment unit is related to the pixel value range of the enhanced adjusted pixel value LDT, it can affect the selection of a and b in the image visual effect improvement unit to a certain extent. This fine adjustment helps to achieve a more accurate and personalized brightness and contrast adjustment scheme, thereby meeting the needs of different application scenarios.

[0029] See also Figures 1 to 4 , after processing, the pixel value CK at the (x, y) position （x，y）In image processing, it is the final adjustment of the visual effect after processing, and it is adjusted according to the dynamic range standards of different images. Taking an 8-bit image as an example, its dynamic range of brightness is 0 to 255, so 0-255 is the reference range of the width and narrowness of the image brightness; The formula for reflecting the brightness width based on the image visual effect improvement unit and the adaptive histogram equalization and feedback adjustment unit is as follows: LF=LDT max -LDT min ; LF is the brightness width value; Based on the brightness width value LF and the image brightness width reference value 255, the image processing is further analyzed and adjusted as follows: S1. If the result value of the brightness width value LF is below 150 in the range of 0-255, it indicates that the brightness range of the image is narrow and the contrast of the image is low. The adaptive adjustment factor ZSY of the adaptive histogram equalization and feedback adjustment unit should be adjusted and applied to the adaptive histogram equalization and feedback adjustment unit for calculation. The adjustment formula of the adaptive adjustment factor ZSY is as follows: ZSY=[1+(c×LDT max -LDT min ) / (LDT （x，y） -LDT min )]; c is a scaling factor, which is used to enhance the visual effect of histogram equalization and the contrast of the image; S2. If the result value of the brightness width value LF is above 150 and below 255 in the range of 0-255, it indicates that the brightness range of the image is wide. When the current adaptive adjustment factor ZSY is maintained, the pixel value CK at the (x, y) position is output. （x，y） .

[0030] In this embodiment, the algorithm unit further adjusts the adaptive adjustment factor ZSY based on the judgment of the width of the brightness range to ensure the visual effect of the image; First, the adaptive adjustment factor ZSY can dynamically adjust the intensity of histogram equalization according to the result of the image visual effect improvement unit. This means that if the brightness range of the image is narrow and the contrast is insufficient, the adaptive adjustment factor ZSY will correspondingly enhance the effect of histogram equalization to expand the brightness range of the image and enhance the contrast. This adjustment helps to make the bright and dark parts of the image more distinct, improve the detail expression of the image, and thus optimize the visual effect of the image. In the process of histogram equalization, if it is not properly controlled, the contrast of the image will be over-amplified, resulting in unnatural visual effects such as color distortion and over-sharpening. The introduction of the adaptive adjustment factor ZSY effectively solves this problem. It can adjust the intensity of equalization according to the actual situation of the image, ensuring that while enhancing the contrast, over-processing is avoided, and the naturalness and authenticity of the image are maintained; By optimizing the contrast and brightness distribution of the image, the adaptive adjustment factor ZSY significantly improves the overall quality of the image, which makes the image more visually appealing and also provides better input data for subsequent image processing steps including feature extraction, image segmentation, and object recognition. High-quality images help improve the accuracy and efficiency of these processing steps, thereby promoting the development of the entire image processing pipeline. Another important advantage of the adaptive adjustment factor ZSY is that it enhances the flexibility and adaptability of the image processing process. Since it can dynamically adjust the equalization parameters according to the actual situation of the image, it can process various types of images and meet different visual effect requirements. This flexibility makes the image processing process more versatile and practical, and can be widely used in many fields such as medical image processing, satellite remote sensing image processing, and security surveillance video analysis. Finally, the introduction of the adaptive adjustment factor ZSY also promoted the innovation and development of image processing technology. It inspired researchers to conduct in-depth research and exploration of image processing algorithms, and promoted the continuous emergence of new technologies and methods. These new technologies and methods not only improved the speed and accuracy of image processing, but also brought more possibilities and development space to the field of image processing. In summary, the adaptive adjustment factor ZSY brings multiple beneficial effects in the process of influencing the adaptive histogram equalization and the feedback adjustment unit, and these effects jointly promote the development and application of image processing technology.

[0031] For example 2, please refer to Figures 1 to 4 , center (x, y) position pixel window CK （x，y） The specific instructions using a 3×3 pixel window are as follows; The 3×3 pixel window size is moderate, which can capture the neighborhood information for analysis and processing, and avoid blurring the image details due to a large window; The 3×3 pixel window has simple geometry and symmetry, which makes it easy to implement and optimize in image processing algorithms; In image processing tasks, 3×3 pixel windows are well represented and recognized within a local neighborhood.

[0032] In this embodiment, by considering the neighborhood information of the 3×3 pixel window, the algorithm can be more adaptable to local changes in the image. For example, in noise suppression, noise points can be smoothed according to the similarity of neighborhood pixels while retaining edge information. Compared with global processing, local window processing usually has lower computational complexity because only a small part of the image is processed each time, so the overall amount of calculation can be reduced. Local window processing allows the algorithm to apply different processing strategies according to different areas of the image. For example, in edge detection, edges can be identified based on the gradient changes of pixels within the window.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An image processing system based on artificial intelligence, characterized in that: The specific implementation steps are as follows: The user uses the image input module to input the original image into the system; The system receives the original image data and displays the image using the display module; Based on the original image data, and using the image adjustment processing module, brightness and contrast adjustment, noise suppression and edge enhancement and histogram equalization are performed to gradually improve the image quality displayed on the display module; Among them, a 3×3 pixel window is selected for local processing of noise suppression and edge enhancement; The image adjustment processing module includes an image visual effect improvement unit, a noise and edge enhancement unit and an adaptive histogram equalization and feedback adjustment unit.

2. The image processing system based on artificial intelligence according to claim 1, characterized in that: The device used by the image input module includes a computing device; The equipment used by the display module includes a display device; The device used by the image adjustment processing module includes an input device.

3. The image processing system based on artificial intelligence according to claim 2, characterized in that: The calculation formula of the image visual effect improvement unit is as follows: LDT=a×YST+b; in: LDT is the pixel value after enhancement adjustment; a is the contrast adjustment factor; YST is the original image data value, and YST reflects any channel of the gray value matrix and the RGB color matrix; b is the brightness adjustment value; The specific calculation instructions for a and b are as follows: If a>1, the image contrast is enhanced; If a<1, the image contrast is weakened; If b is a positive value, the image brightness is enhanced; If b is a positive value, the image brightness decreases.

4. The image processing system based on artificial intelligence according to claim 3, characterized in that: The calculation formula of the noise and edge enhancement unit is as follows: ； Ltd. c，avg =(LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） ) / 9; in: LDT c（x，y） Enhance the pixel value for the (x, y) position within the window; CK （x，y） is the pixel window centered at (x, y) position, CK （x，y） Reflects a 3×3 pixel window with (x,y) as the center coordinate axis; LDT （n，m） is the pixel value at position (n, m) in the window; LDT c，avg is the average value of pixels at position (n, m) in the window; d is the edge enhancement coefficient, which is used to adjust the strength of edge enhancement; LDT （x，y） Enhance pixel value for (x, y) position, LDT （x，y） Reflects the enhanced adjusted pixel value LDT at the position (x, y) in the overall image; The average value of the pixel at position (n, m) in the window LDT c，avg The specific calculation instructions are as follows: Since the center (x, y) position of the pixel window CK （x，y） Reflects a 3×3 pixel window with (x, y) as the center coordinate axis, so the pixel value at position (n, m) in the 3×3 pixel window is LDT （n，m） The total amount of calculation is 9; LDT （n1，m1） +LDT （n2，m2） +LDT （n3，m3） ......+LDT （n9，m9） LDT is the pixel value at position (n, m) in the 9 windows （n，m） The sum of .

5. The image processing system based on artificial intelligence according to claim 4, characterized in that: The center (x, y) position of the pixel window CK （x，y） The specific instructions using a 3×3 pixel window are as follows; The 3×3 pixel window size is moderate, which can capture the neighborhood information for analysis and processing, and avoid blurring the image details due to a large window; The 3×3 pixel window has simple geometry and symmetry, which makes it easy to implement and optimize in image processing algorithms; In image processing tasks, 3×3 pixel windows are well represented and recognized within a local neighborhood.

6. The image processing system based on artificial intelligence according to claim 5, characterized in that: The calculation formula of the adaptive histogram equalization and feedback adjustment unit is as follows: CK （x，y） =ZTH(LDT c（x，y） )×ZSY; ZSY=[1+(LDT max -LDT min ) / (LDT （x，y） -LDT min )]; in: CK （x，y） is the pixel value at the (x, y) position after processing; ZTH is a histogram equalization function, which is used to improve the contrast of the image; ZSY is the adaptive adjustment factor; LDT min The minimum pixel value after enhancement adjustment; LDT max The maximum pixel value after enhancement adjustment.

7. The image processing system based on artificial intelligence according to claim 5, characterized in that: The pixel value CK at the (x, y) position after the processing （x，y） In image processing, it is the final adjustment of the visual effect after processing, and it is adjusted according to the dynamic range standards of different bit images. Taking an 8-bit image as an example, its dynamic range of brightness is 0 to 255, so 0-255 is the reference range of the width and narrowness of the image brightness; The formula reflecting the brightness width based on the image visual effect improvement unit and the adaptive histogram equalization and feedback adjustment unit is as follows: LF=LDT max -LDT min ; LF is the brightness width value.

8. The image processing system based on artificial intelligence according to claim 7, characterized in that: Based on the brightness width value LF and the image brightness width reference value 255, the image processing is further analyzed and adjusted as follows: S1. If the result value of the brightness width value LF is below 150 in the range of 0-255, it indicates that the brightness range of the image is narrow and the contrast of the image is low. The adaptive adjustment factor ZSY of the adaptive histogram equalization and feedback adjustment unit should be adjusted and applied to the adaptive histogram equalization and feedback adjustment unit for calculation. The adjustment formula of the adaptive adjustment factor ZSY is as follows: ZSY=[1+(c×LDT max -LDT min ) / (LDT （x，y） -LDT min )]; c is a scaling factor, which is used to enhance the visual effect of histogram equalization and the contrast of the image; S2. If the result value of the brightness width value LF is above 150 and below 255 in the range of 0-255, it indicates that the brightness range of the image is wide. When the current adaptive adjustment factor ZSY is maintained, the pixel value CK at the (x, y) position is output. （x，y） .

9. The image processing system based on artificial intelligence according to claim 2, characterized in that: The computing device includes a CPU, which is used to perform basic mathematical operations and logical judgments. The display device includes a display, which is used to display original images and processed images. The input device includes a keyboard, a mouse and a touch screen, and the input device is used for users to input parameters, select processing options and interact with the system.

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