AI image processing system and method

Abstract

The present invention discloses an AI image processing system and method, which relate to the field of AI image processing technology, and include an image collection module, an image feature extraction module, an image processing method detection module, an image processing method recommendation module, an image processing parameter recommendation module, and an image processing module; the image collection module is used to acquire images, select unprocessed images to save, obtain original images, and select processed images corresponding to the original images to save; the AI ​​image processing system and method can discover and identify processing features of processed images through training models, so as to quickly obtain processing methods for processed images, which is convenient for subsequent training models using processing methods; and can discover the relationship between processed images with good display effects and the processing methods and processing parameters used for original images through training models, so that the model can obtain recommended processing methods and processing parameters through the original images, quickly process the images, and improve the display effects of the images.

Description

Technical Field

[0001] The present invention relates to the field of AI image processing technology, and in particular to an AI image processing system and method. Background Art

[0002] Image processing refers to the technology of using computers to analyze images to achieve the desired results. It generally includes three parts: image compression, enhancement and restoration, matching, description and recognition. In order for images to be presented to people with better display and viewing effects, it is generally necessary to process the images.

[0003] The prior art of publication number CN117611578A discloses an image processing method and an image processing system, which relate to the field of image processing technology. When the system is running, image acquisition is performed through an image acquisition module to obtain original image data, feature extraction is performed through an extraction module to obtain brightness information and pixel information in the original image, the brightness information and pixel information are preprocessed through a preprocessing module to form a first data set and a second data set, and calculation is performed through an image calculation module to obtain: a visual anomaly index Sjzs, which is matched with a preset visual anomaly threshold S through an evaluation module to obtain a visual anomaly evaluation strategy scheme, and finally, the content of the visual anomaly evaluation strategy scheme is specifically executed and notified through an execution module, thereby detecting visual problems of light spots and shadows appearing in the captured image, and providing personalized adjustment suggestions, so as to achieve the purpose of optimizing image quality, improving work efficiency, and reducing the number of device shots.

[0004] However, in the process of image processing, how to determine the image processing method and processing parameters so as to automatically process the image to achieve better display effects, reduce the difficulty of image processing, and simplify the image processing steps is a problem that needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide an AI image processing system and method to address the above-mentioned deficiencies in the prior art.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an AI image processing system, comprising an image collection module, an image feature extraction module, an image processing method detection module, an image processing method recommendation module, an image processing parameter recommendation module, and an image processing module;

[0007] The image collection module is used to acquire images, select an unprocessed image to save, obtain an original image, and select a processed image corresponding to the original image to save, wherein, when selecting the processed image, a processed image with a better display effect is manually selected and saved corresponding to the original image;

[0008] The image feature extraction module is used to extract features from the original image and the processed image, and can select pre-trained edge detection algorithms, VGGNet, ResNet, Inception, SIFT, SURF, ORB, etc.

[0009] The image processing mode detection module is used to select an original image as a template image and set multiple image processing modes, respectively use the image processing modes to process the template images to obtain corresponding template-processed images, analyze the difference between the template-processed images and the corresponding template images to obtain difference features of each image processing mode, train a first machine learning model based on the template-processed images, the difference features, and the image processing mode to obtain a processing mode detection model, the processing mode detection model is used to identify the difference features in the input processing image and the pixel coordinates of the difference features in the processing image to obtain the difference coordinates, and output the corresponding image processing mode and the difference coordinates;

[0010] The image processing method recommendation module is used to obtain image features of an original image corresponding to a processed image at a difference coordinate position of the processed image, obtain processing position features of the original image, train a second machine learning model based on the original image, the image processing method, and the processing position features, and obtain a processing method recommendation model. The processing method recommendation model is used to obtain a recommended image processing method based on the input original image, detect the processing position features, and output processing position coordinates. The processing position coordinates are the pixel coordinates of the processing position features, and the pixel coordinates of the processing position features in the original image can be directly obtained through the detected processing position features.

[0011] The image processing parameter recommendation module is used to analyze the difference between the processing position characteristics of each original image and the difference characteristics of the corresponding processed image to obtain difference characteristic parameters, and train a third machine learning model based on the original image, the image processing method and the difference characteristic parameters to obtain a processing parameter prediction model. The processing parameter prediction model is used to output predicted difference characteristic parameters based on the input original image and image processing method;

[0012] The image processing module is used to obtain a new original image, input the new original image into the processing method recommendation model to obtain the corresponding image processing method and processing position coordinates, and then input the new original image and the obtained image processing method into the processing parameter prediction model to obtain corresponding difference feature parameters, and process the new original image based on the obtained image processing method, processing position coordinates and difference feature parameters to obtain a corresponding new processed image.

[0013] Furthermore, the image processing method detection module is used to select an original image as a template image, set multiple image processing methods, use the image processing methods to process the template image respectively, obtain corresponding template-processed images, analyze the difference between the template-processed image and the corresponding template image, obtain difference features of each image processing method, and train a first machine learning model based on the template-processed image, the difference features, and the image processing method to obtain a processing method detection model, including the following steps:

[0014] Select the original image as the template image and set various image processing methods;

[0015] Using each image processing method to process the template image respectively to obtain a corresponding template-processed image;

[0016] Based on the template image and the template-processed image, obtaining a processed difference image between the template image and the template-processed image, wherein a difference image can be generated by subtracting the template-processed image from the template image, wherein areas with zero pixel values ​​indicate that the two images are identical, and non-zero areas indicate processed areas, and an image of the processed area in the template-processed image is selected as the processed difference image;

[0017] grayscale the processed difference image, and process the grayscale processed difference image using the image feature extraction module to obtain corresponding difference features;

[0018] Setting a plurality of processing method labels to correspond one to one with the image processing methods, and associating the difference features with the corresponding processing method labels;

[0019] Based on the difference features, template-processed image and image processing labels, a first machine learning model is trained to obtain a processing method detection model. During training, the template-processed image is used as input, and the difference features and image processing labels are used as output, wherein the difference features are the output of the hidden layer, and the processing labels are the output of the output layer. The processing method detection model is used to detect the difference features in the processed image based on the input processed image, obtain the pixel coordinates of the difference features in the processed image to obtain the difference coordinates, and output the processing method label associated with the difference features and the difference coordinates.

[0020] The present invention is not limited to specific machine learning models, such as convolutional neural networks (CNN), support vector machines (SVM), random forests (Random Forest) generative adversarial networks (GANs), etc.

[0021] Furthermore, the system also includes a processing method verification module, which is used for the user to select the user to verify that the relationship is correct, and save the processed image and the corresponding processing method label and pixel coordinates as a sample.

[0022] Furthermore, the processing method recommendation module is also used to use the samples to train and update the processing method detection model when the samples reach a set first sample quantity.

[0023] Furthermore, the image processing method recommendation module trains a second machine learning model based on the original image, the image processing method, and the processing position features to obtain a processing method recommendation model. The processing method recommendation model is used to obtain a recommended image processing method based on the input original image, including the following steps:

[0024] Select the original image for which the corresponding processed image exists;

[0025] Obtaining image processing methods of an original image and a corresponding processed image, and obtaining a processing method label corresponding to the image processing method;

[0026] Input the original image into the image feature extraction module to obtain the corresponding original image features;

[0027] Based on the original image features, the processing method labels and the processing position features, a second machine learning model is trained to obtain a processing method recommendation model. During training, the original image features are used as input, and the processing position features and the processing method labels are used as output, wherein the processing position features are the hidden layer outputs, and the processing method labels are the output layer outputs. The model is used to extract the original image features from the input original image through the image feature extraction module, retrieve the corresponding processing position features based on the original image features, obtain the pixel coordinates of the processing position features to obtain the processing position coordinates, and output the corresponding processing method labels and the processing position coordinates.

[0028] Furthermore, the image processing parameter recommendation module trains a third machine learning model based on the original image, the image processing method, and the difference feature parameters to obtain a processing parameter prediction model, including the following steps:

[0029] Input the original image into the image feature extraction module to obtain the corresponding original image features;

[0030] Classify the original image and the corresponding processed image according to different processing methods to obtain multiple processing groups;

[0031] Extracting the processing position feature and the difference feature corresponding to the processing method corresponding to each processing group, respectively, and obtaining difference feature parameters of the difference feature and the corresponding difference position feature;

[0032] Based on the original image features and difference feature parameters of each processing group, a third machine learning model is trained to obtain a processing parameter prediction sub-model, wherein the original image features are used as input and the difference feature parameters are used as output;

[0033] The processing parameter prediction sub-models of all processing groups are merged to obtain a processing parameter prediction model. The processing parameter prediction model selects the corresponding processing parameter prediction sub-model according to the input processing method label, extracts the original image features through the image feature extraction module, and outputs the corresponding difference feature parameters based on the original image features.

[0034] An AI image processing method comprises the following steps:

[0035] S1. Select the original image and the corresponding processed image through the image collection module and save them for later use;

[0036] S2. Extract features from the original image and the processed image through an image feature extraction module;

[0037] S3. Train the first machine learning model through the image processing mode detection module to obtain a processing mode detection model;

[0038] S4. Training a second machine learning model through the image processing method recommendation module to obtain a processing method recommendation model;

[0039] S5. Train the third machine learning model through the image processing parameter recommendation module to obtain a processing parameter prediction model;

[0040] S6. A new original image to be processed is inputted through the image collection module, and the image processing module processes the new original image to obtain a new processed image through the processing method recommendation model and the processing parameter prediction model.

[0041] Compared with the existing technology, the present invention provides an AI image processing system and method, which can identify the processing features of the processed image through the training model by setting an image collection module, an image feature extraction module, an image processing method detection module, an image processing method recommendation module, an image processing parameter recommendation module, and an image processing module, so as to quickly obtain the processing method of the processed image, which is convenient for subsequent training of the model using the processing method; and discover the relationship between the processed image with good display effect and the processing method and processing parameters used in the original image through the training model, so that the model can obtain the recommended processing method and processing parameters through the original image, quickly process the image, and improve the display effect of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0043] Figure 1A block diagram of the system structure provided by an embodiment of the present invention;

[0044] Figure 2 A diagram of the method steps provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0046] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention.

[0047] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a communication between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0048] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art.

[0049] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0050] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0051] The terms used herein are used only to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a," "an," and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements, and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof is not excluded.

[0052] The embodiments described herein may be described with reference to plan views and / or cross-sectional views, with the aid of idealized schematic diagrams of the present disclosure. Thus, the example illustrations may be modified based on manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings are schematic in nature, and the shapes of the regions shown in the drawings illustrate specific shapes of the regions of the elements, but are not intended to be limiting.

[0053] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined as such herein.

[0054] See also Figure 1 , an AI image processing system, including an image collection module, an image feature extraction module, an image processing method detection module, an image processing method recommendation module, an image processing parameter recommendation module, and an image processing module;

[0055] The image collection module is used to obtain images, select unprocessed images to save, obtain original images, and select processed images corresponding to the original images to save. When selecting processed images, a processed image with better display effect is manually selected and saved corresponding to the original image.

[0056] The image feature extraction module is used to extract features from original and processed images. You can choose pre-trained edge detection algorithms, VGGNet, ResNet, Inception, SIFT, SURF, ORB, etc.

[0057] The image processing method detection module is used to select the original image as the template image and set multiple image processing methods. The template image is processed using the image processing methods respectively to obtain the corresponding template-processed image. The difference between the template-processed image and the corresponding template image is analyzed to obtain the difference features of each image processing method. Based on the template-processed image, the difference features, and the image processing method, a first machine learning model is trained to obtain a processing method detection model. The processing method detection model is used to identify the difference features in the input processing image and the pixel coordinates of the difference features in the processing image, obtain the difference coordinates, and output the corresponding image processing method and the difference coordinates. The steps include:

[0058] (1) Select the original image as the template image and set various image processing methods;

[0059] (2) performing image processing on the template image using each image processing method to obtain a corresponding template-processed image;

[0060] (3) based on the template image and the template-processed image, obtaining a processed difference image between the template image and the template-processed image, wherein a difference image can be generated by subtracting the template-processed image from the template image, wherein areas with zero pixel values ​​indicate that the two images are identical, and non-zero areas indicate processed areas, and an image of the processed area in the template-processed image is selected as the processed difference image;

[0061] (4) graying the processed difference image, and using the image feature extraction module to process the grayed processed difference image to obtain corresponding difference features;

[0062] (5) setting a plurality of processing method labels corresponding to the image processing methods, and associating the difference features with the corresponding processing method labels;

[0063] (6) Based on the difference features, the template-processed image and the image processing label, a first machine learning model is trained to obtain a processing method detection model. During training, the template-processed image is used as input, and the difference features and the image processing label are used as output, wherein the difference features are the output of the hidden layer and the processing label is the output of the output layer. The processing method detection model is used to detect the difference features in the processed image based on the input processed image, obtain the pixel coordinates of the difference features in the processed image to obtain the difference coordinates, and output the processing method label and the difference coordinates associated with the difference features.

[0064] The present invention is not limited to specific machine learning models, such as convolutional neural networks (CNN), support vector machines (SVM), random forests (Random Forest) generative adversarial networks (GANs), etc.

[0065] The image processing method recommendation module is used to obtain the image features of the original image corresponding to the processed image at the difference coordinate position of the processed image, obtain the processing position feature of the original image, train the second machine learning model based on the original image, and obtain the processing method recommendation model. The processing method recommendation model is used to obtain the recommended image processing method based on the input original image, detect the processing position feature, and output the processing position coordinates. The processing position coordinates are the pixel coordinates of the processing position feature. The pixel coordinates in the original image can be directly obtained through the detected processing position feature. The module includes the following steps:

[0066] (1) Selecting an original image that has a corresponding processed image;

[0067] (2) obtaining image processing methods of the original image and the corresponding processed image, and obtaining a processing method label corresponding to the image processing method;

[0068] (3) Inputting the original image into the image feature extraction module to obtain the corresponding original image features;

[0069] (4) Based on the original image features, the processing method label and the processing position features, a second machine learning model is trained to obtain a processing method recommendation model. During the training, the original image features are used as input, and the processing position features and the processing method label are used as output, wherein the processing position features are output from the hidden layer, and the processing method label is output from the output layer. The model is used to extract the original image features from the input original image through the image feature extraction module, retrieve the corresponding processing position features based on the original image features, obtain the pixel coordinates of the processing position features to obtain the processing position coordinates, and output the corresponding processing method label and processing position coordinates.

[0070] The system also includes a processing method verification module, which is used for the user to select and verify that the relationship is correct, and the processed image and the corresponding processing method label and pixel coordinates are saved as samples.

[0071] The processing method recommendation module is further configured to use the samples to train and update the processing method detection model when the number of samples reaches a set first number of samples.

[0072] The image processing parameter recommendation module is used to analyze the difference between the processing position characteristics of each original image and the difference characteristics of the corresponding processed image to obtain difference characteristic parameters. Based on the original image, image processing method and difference characteristic parameters, a third machine learning model is trained to obtain a processing parameter prediction model. The processing parameter prediction model is used to output predicted difference characteristic parameters based on the input original image and image processing method, including the following steps:

[0073] (1) Input the original image into the image feature extraction module to obtain the corresponding original image features;

[0074] (2) classifying the original image and the corresponding processed image according to different processing methods to obtain multiple processing groups;

[0075] (3) extracting the treatment position features and difference features corresponding to the treatment methods of each treatment group, and obtaining the difference feature parameters of the difference features and the corresponding difference position features;

[0076] (4) training a third machine learning model based on the original image features and difference feature parameters of each processing group to obtain a processing parameter prediction sub-model, wherein the original image features are used as input and the difference feature parameters are used as output during training;

[0077] (5) The processing parameter prediction sub-models of all processing groups are merged to obtain a processing parameter prediction model. The processing parameter prediction model selects the corresponding processing parameter prediction sub-model according to the input processing method label, extracts the original image features through the image feature extraction module, and outputs the corresponding difference feature parameters based on the original image features.

[0078] The image processing module is used to obtain a new original image, input the new original image into the processing method recommendation model, obtain the corresponding image processing method and processing position coordinates, and then input the new original image and the obtained image processing method into the processing parameter prediction model to obtain the corresponding difference feature parameters. Based on the obtained image processing method, processing position coordinates and difference feature parameters, the new original image is processed to obtain the corresponding new processed image.

[0079] See also Figure 2 , an AI image processing method, comprising the following steps:

[0080] S1. Select the original image and the corresponding processed image through the image collection module and save them for later use;

[0081] S2. Extract features from the original image and the processed image through an image feature extraction module;

[0082] S3. Train the first machine learning model through the image processing mode detection module to obtain a processing mode detection model;

[0083] S4. Training a second machine learning model through the image processing method recommendation module to obtain a processing method recommendation model;

[0084] S5. Train the third machine learning model through the image processing parameter recommendation module to obtain a processing parameter prediction model;

[0085] S6. The new original image to be processed is inputted through the image collection module, and the image processing module processes the new original image to obtain a new processed image through the processing method recommendation model and the processing parameter prediction model.

[0086] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. An AI image processing system, characterized by: It includes image collection module, image feature extraction module, image processing method detection module, image processing method recommendation module, image processing parameter recommendation module and image processing module; The image collection module is used to acquire images, select an unprocessed image to save, obtain an original image, and select a processed image corresponding to the original image to save; The image feature extraction module is used to extract features from the original image and the processed image; The image processing mode detection module is used to select an original image as a template image and set multiple image processing modes, respectively use the image processing modes to process the template images to obtain corresponding template-processed images, analyze the difference between the template-processed images and the corresponding template images to obtain difference features of each image processing mode, train a first machine learning model based on the template-processed images, the difference features, and the image processing mode to obtain a processing mode detection model, the processing mode detection model is used to identify the difference features in the input processing image and the pixel coordinates of the difference features in the processing image to obtain the difference coordinates, and output the corresponding image processing mode and the difference coordinates; The image processing method recommendation module is used to obtain image features of an original image corresponding to a processed image at a difference coordinate position of the processed image, obtain processing position features of the original image, train a second machine learning model based on the original image, the image processing method, and the processing position features, and obtain a processing method recommendation model. The processing method recommendation model is used to obtain a recommended image processing method based on the input original image, detect the processing position features, and output processing position coordinates. The processing position coordinates are the pixel coordinates of the processing position features, and the pixel coordinates of the processing position features in the original image can be directly obtained through the detected processing position features. The image processing parameter recommendation module is used to analyze the difference between the processing position characteristics of each original image and the difference characteristics of the corresponding processed image to obtain difference characteristic parameters, and train a third machine learning model based on the original image, the image processing method and the difference characteristic parameters to obtain a processing parameter prediction model. The processing parameter prediction model is used to output predicted difference characteristic parameters based on the input original image and image processing method; The image processing module is used to obtain a new original image, input the new original image into the processing method recommendation model to obtain the corresponding image processing method and processing position coordinates, and then input the new original image and the obtained image processing method into the processing parameter prediction model to obtain corresponding difference feature parameters, and process the new original image based on the obtained image processing method, processing position coordinates and difference feature parameters to obtain a corresponding new processed image.

2. The AI ​​image processing system according to claim 1, wherein: The image processing mode detection module is used to select an original image as a template image and set multiple image processing modes, respectively use the image processing modes to process the template images to obtain corresponding template-processed images, analyze the difference between the template-processed images and the corresponding template images, obtain difference features of each image processing mode, and train a first machine learning model based on the template-processed images, the difference features, and the image processing modes to obtain a processing mode detection model, including the following steps: Select the original image as the template image and set various image processing methods; Using each image processing method to process the template image respectively to obtain a corresponding template-processed image; Based on the template image and the template-processed image, obtaining a processed difference image between the template image and the template-processed image; grayscale the processed difference image, and process the grayscale processed difference image using the image feature extraction module to obtain corresponding difference features; Setting a plurality of processing method labels to correspond one to one with the image processing methods, and associating the difference features with the corresponding processing method labels; Based on the difference features, template-processed image and image processing labels, a first machine learning model is trained to obtain a processing method detection model. During training, the template-processed image is used as input, and the difference features and image processing labels are used as output. The processing method detection model is used to detect the difference features in the processed image based on the input processed image, obtain the pixel coordinates of the difference features in the processed image to obtain the difference coordinates, and output the processing method label associated with the difference features and the difference coordinates.

3. The AI ​​image processing system according to claim 2, wherein: The system further includes a processing method verification module, which is used for the user to select the user to verify that the relationship is correct, and to save the processed image and the corresponding processing method label and pixel coordinates as a sample.

4. The AI ​​image processing system according to claim 3, wherein: The processing method recommendation module is further configured to use the samples to train and update the processing method detection model when the number of samples reaches a set first number of samples.

5. The AI ​​image processing system according to claim 1, wherein: The image processing method recommendation module trains a second machine learning model based on the original image, the image processing method, and the processing position features to obtain a processing method recommendation model. The processing method recommendation model is used to obtain a recommended image processing method based on the input original image, including the following steps: Select the original image for which the corresponding processed image exists; Obtaining image processing methods of an original image and a corresponding processed image, and obtaining a processing method label corresponding to the image processing method; Input the original image into the image feature extraction module to obtain the corresponding original image features; Based on the original image features, the processing method label and the processing position features, a second machine learning model is trained to obtain a processing method recommendation model. During training, the original image features are used as input, and the processing position features and the processing method label are used as output. The model is used to extract the original image features from the input original image through the image feature extraction module, retrieve the corresponding processing position features based on the original image features, obtain the pixel coordinates of the processing position features to obtain the processing position coordinates, and output the corresponding processing method label and the processing position coordinates.

6. The AI ​​image processing system according to claim 1, wherein: The image processing parameter recommendation module trains a third machine learning model based on the original image, the image processing method, and the difference feature parameters to obtain a processing parameter prediction model, including the following steps: Input the original image into the image feature extraction module to obtain the corresponding original image features; Classify the original image and the corresponding processed image according to different processing methods to obtain multiple processing groups; Extracting the processing position feature and the difference feature corresponding to the processing method corresponding to each processing group, respectively, and obtaining difference feature parameters of the difference feature and the corresponding difference position feature; Based on the original image features and difference feature parameters of each processing group, a third machine learning model is trained to obtain a processing parameter prediction sub-model, wherein the original image features are used as input and the difference feature parameters are used as output; The processing parameter prediction sub-models of all processing groups are merged to obtain a processing parameter prediction model. The processing parameter prediction model selects the corresponding processing parameter prediction sub-model according to the input processing method label, extracts the original image features through the image feature extraction module, and outputs the corresponding difference feature parameters based on the original image features.

7. An AI image processing method, applicable to an AI image processing system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Select the original image and the corresponding processed image through the image collection module and save them for later use; S2. Extract features from the original image and the processed image through an image feature extraction module; S3. Train the first machine learning model through the image processing mode detection module to obtain a processing mode detection model; S4. Training a second machine learning model through the image processing method recommendation module to obtain a processing method recommendation model; S5. Train the third machine learning model through the image processing parameter recommendation module to obtain a processing parameter prediction model; S6. A new original image to be processed is inputted through the image collection module, and the image processing module processes the new original image to obtain a new processed image through the processing method recommendation model and the processing parameter prediction model.

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