Film image object recognition and automatic coloring repair system based on deep learning

Abstract

The invention discloses a deep learning-based movie image object recognition and automatic coloring restoration system, which relates to the technical field of image recognition and comprises an image input module for receiving a to-be-restored movie image frame; the object recognition module is used for recognizing a specific object appearing in the image frame based on a deep learning algorithm; the color mapping module is used for coloring and repairing the identified object; the repairing processing module is used for repairing the recognized and colored object based on an automatic algorithm and recovering the color and details of the object; and the image output module is used for outputting the repaired movie image frame. According to the method, the object in the image is automatically recognized and colored and repaired, the complexity and low efficiency of the traditional manual repairing process are avoided, the color and details of the object can be intelligently recovered by the system according to the object features in the image through the deep learning model, feature extraction and object detection are carried out through the multilayer convolutional neural network, and the image quality is improved. The system can efficiently identify the specific object in the image.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a movie image object recognition and automatic coloring and restoration system based on deep learning. Background Art

[0002] With the rapid development of deep learning technology, the field of image processing has made significant progress in many applications, especially in image restoration, object recognition, image colorization, etc. As a special type of image, movie images often contain complex scenes and object details. Therefore, for the restoration and colorization of movie images, traditional image processing methods often cannot meet the requirements of high precision and high quality. In the process of movie image restoration, due to the passage of time, many classic movie images may be damaged or faded, resulting in loss of image details and color, especially in the processing of black and white movies or old movies. For these damaged images, traditional manual restoration methods are not only inefficient, but also have poor restoration effects, and cannot automatically identify and restore specific objects and colors in movies.

[0003] At present, many image restoration technologies based on deep learning focus on edge restoration, texture filling, color mapping and detail restoration. However, there are still some challenges for tasks such as automatic object recognition, color mapping and color restoration of movie images, such as how to accurately identify specific objects in the image, how to adjust the color in combination with contextual information, how to restore the details of the object and lighting effects, etc. Therefore, the present invention proposes a movie image object recognition and automatic color restoration system based on deep learning. The system uses the advantages of deep learning to automatically identify specific objects in movie images and restore and color them, which can achieve efficient and accurate movie image restoration, especially the restoration of old movie images. Summary of the invention

[0004] In order to solve the above technical problems, a movie image object recognition and automatic coloring restoration system based on deep learning is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A movie image object recognition and automatic color restoration system based on deep learning, comprising:

[0007] Image input module: The image input module is used to receive the movie image frame to be restored;

[0008] Object recognition module: The object recognition module is electrically connected to the image input module. The object recognition module is used to recognize specific objects appearing in the image frame based on a deep learning algorithm. The specific objects are objects with specific structures and features that appear repeatedly in the movie.

[0009] Color mapping module: The color mapping module is electrically connected to the object recognition module, and is used to perform color restoration on the recognized object through the model according to known object color information;

[0010] Repair processing module: The repair processing module is electrically connected to the color mapping module, and is used to repair the identified and colored objects based on an automated algorithm to restore their colors and details;

[0011] Image output module: The image output module is electrically connected to the restoration processing module, and the image output module is used to output the restored movie image frame.

[0012] Preferably, the image input module specifically includes:

[0013] Image acquisition unit: receiving the movie image frames to be restored, the image frames are extracted from the video files and input through the real-time video stream;

[0014] Preprocessing unit: performs preliminary preprocessing on the received movie images, including denoising, grayscale and contrast adjustment;

[0015] Frame processing unit: decomposes the movie video into independent image frames, and each frame is analyzed and repaired as an independent processing unit;

[0016] Size normalization unit: performs size normalization on the input image frames to ensure that all input images have the same resolution and size;

[0017] Data format conversion unit: According to the requirements of subsequent modules, the input image is converted into a matrix data format and the received image frames are temporarily stored.

[0018] Preferably, the object recognition module comprises:

[0019] Feature extraction unit: Based on the deep learning object recognition model, multiple convolution layers are used to extract features. The feature extraction process mainly includes convolution operations and pooling operations to extract local features of the image, including edges, textures, and colors.

[0020] Category output unit: Based on recurring objects with specific structures and features, a convolutional neural network is used to train the object recognition model. Through multiple layers of convolution, activation, pooling, and fully connected layers, the spatial features in the image are learned and the object categories in the image are output.

[0021] Object detection and classification unit: detects specific objects in image frames, locates objects, and classifies objects;

[0022] Post-processing unit: After the object recognition module completes object detection, the non-maximum suppression method is used to eliminate duplicate detection frames.

[0023] Preferably, the feature extraction unit specifically includes:

[0024] Based on the convolutional neural network, feature extraction is performed through multiple convolutional layers. The convolutional neural network uses the convolution kernel to perform convolution operations on the image to extract the local features of the image, including edges, textures, and colors. The convolution operation formula is:

[0025]

[0026] Where I is the input image, K is the convolution kernel, (m, n) is the coordinate of the convolution kernel, K(m, n) is the convolution kernel, (x, y) is the position coordinate in the output feature map, and (I·K)(x, y) represents the pixel value of the output image at position (x, y) after the convolution operation.

[0027] Based on the pooling operation, the maximum pooling method is used to reduce the size of the feature map, reduce the amount of calculation, and retain important feature information. The formula for the maximum pooling operation is:

[0028]

[0029] Among them, P(x, y) represents the pixel value at position (x, y) in the output image after the pooling operation, I(x+m, y+n) represents the pixel value at position (x+m, y+n) in the input image, and max m，n Indicates that the maximum value within the pooling window is taken during the pooling operation.

[0030] Preferably, the object detection and classification unit specifically includes:

[0031] Detect specific objects in the image frame, locate the objects through the output of the model, and locate the position of the objects in the image based on the bounding box coordinates output by the model;

[0032] Perform object classification and identify the object categories in the image. The model outputs a probability distribution, which is the probability that the object in the image belongs to each category, and selects the category with the highest probability as the predicted category of the object.

[0033] Preferably, the post-processing unit specifically includes:

[0034] After the object recognition module completes object detection, non-maximum suppression post-processing technology is used to eliminate duplicate detection frames and retain the most likely detection frames to prevent the same object from being recognized multiple times. The algorithm steps of non-maximum suppression are as follows:

[0035] Sort bounding boxes by classification confidence;

[0036] Select the bounding box with the highest confidence as the reference;

[0037] Remove the boxes that have a large overlap with the reference box, where the formula for calculating the intersection of two bounding boxes is:

[0038]

[0039] In the formula, A represents the predicted area, B represents the true area, |A∩B| represents the area of ​​the intersection of A and B, |A∪B| represents the area of ​​the union of A and B, and IOU is the evaluation indicator used to measure the two areas.

[0040] Preferably, the color mapping module specifically includes:

[0041] Output acquisition unit of object recognition module: The object recognition module performs object recognition based on input data, identifies each object in the image and their category and location;

[0042] Object category information: The identified objects are classified into different categories, including "apple", "car" and "chair";

[0043] Position and shape information: The specific position and shape information of the object, including the coordinates of the bounding box and a more precise segmentation mask, will also be passed to the color mapping module;

[0044] Color information matching unit: Based on the category and context information of the object, the color mapping module matches the typical color of the object in the category from the database;

[0045] Object region coloring unit: Extracts the region of the object in the image based on the result of object recognition and defines it by means of bounding box and segmentation mask.

[0046] Preferably, the object area coloring unit specifically includes:

[0047] Color mapping and repair: Apply the selected color information to the object area for coloring. The color of the object in the image has been partially lost, so repair is performed to restore the typical color of the object. The repair process includes:

[0048] Color filling: If the color of the identified area is lost or inaccurate, the color is repaired through the filling algorithm;

[0049] Model-based color adjustment: If there are changes in lighting, shadows, etc. on the surface of an object, the color mapping module needs to take these factors into account and make appropriate color adjustments;

[0050] Color mapping optimization and fusion: Perform color smoothing, use interpolation to make a smooth transition between the object color and the background color, and perform color correction based on the object's spatial position and lighting information.

[0051] Preferably, the repair processing module specifically includes:

[0052] Object recognition and coloring information acquisition unit: receives the colored object information from the color mapping module, and identifies the area, boundary and missing texture details of the object;

[0053] Image restoration area analysis unit: Determine the edge position of the object based on the edge detection algorithm, identify the object outline, check whether there is any missing texture and details, and use filling and interpolation algorithms to supplement them;

[0054] Detail recovery and texture restoration unit: restores these details based on the texture restoration algorithm, fills the image based on the surrounding pixels, and is used to restore texture and color details, enhance the areas where the texture is missing or blurred, and increase the clarity of the details;

[0055] Color restoration and adjustment unit: local adjustment of color based on the object's ambient lighting, shadow area, and other contextual information, adjusting the global and local color balance of the image;

[0056] Detail enhancement and lighting restoration unit: Perform lighting restoration according to the lighting conditions on the object surface, restore natural lighting effects, and adjust the brightness and contrast of the image through HDR technology.

[0057] Preferably, the image restoration region analysis unit specifically includes:

[0058] Based on the edge detection algorithm, the edge position of the object is determined and the outline of the object is identified. The edge detection algorithm formula is:

[0059]

[0060] In the formula, G represents the gradient size at a certain position in the image, G x Represents the horizontal gradient of a certain position in the image, G y It represents the vertical gradient of a certain position in the image, θ represents the gradient direction of a certain position in the image, and atan2 is a mathematical function.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention proposes a method for automatically identifying objects in images and coloring and repairing them, avoiding the tediousness and inefficiency of the traditional manual repair process. Through the deep learning model, the system can intelligently restore the color and details of the object according to the object features in the image. Through the multi-layer convolutional neural network for feature extraction and object detection, the system can efficiently identify specific objects in the image, especially those that appear repeatedly in movies and have specific structures and features. Based on these recognition results, the system can accurately locate and classify objects, providing accurate input for subsequent repair and coloring. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a system framework diagram of the present invention;

[0064] Figure 2 This is an internal system framework diagram of the image input module in the present invention;

[0065] Figure 3 This is an internal system framework diagram of the object recognition module in the present invention;

[0066] Figure 4 The internal system framework diagram of the color mapping module in the present invention;

[0067] Figure 5 This is an internal system framework diagram of the repair processing module in the present invention. DETAILED DESCRIPTION

[0068] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0069] Reference Figure 1 As shown in the figure, the deep learning-based movie image object recognition and automatic coloring restoration system includes:

[0070] Step 1:

[0071] Image acquisition unit: receiving the movie image frames to be restored, the image frames are extracted from the video files and input through the real-time video stream;

[0072] Preprocessing unit: performs preliminary preprocessing on the received movie images, including denoising, grayscale and contrast adjustment;

[0073] Frame processing unit: decomposes the movie video into independent image frames, and each frame is analyzed and repaired as an independent processing unit;

[0074] Size normalization unit: performs size normalization on the input image frames to ensure that all input images have the same resolution and size;

[0075] Data format conversion unit: According to the requirements of subsequent modules, the input image is converted into a matrix data format and the received image frames are temporarily stored.

[0076] Step 2:

[0077] Feature extraction unit: Based on the deep learning object recognition model, multiple convolution layers are used to extract features. The feature extraction process mainly includes convolution operations and pooling operations to extract local features of the image, including edges, textures, and colors.

[0078] Based on the convolutional neural network, feature extraction is performed through multiple convolutional layers. The convolutional neural network uses the convolution kernel to perform convolution operations on the image to extract the local features of the image, including edges, textures, and colors. The convolution operation formula is:

[0079]

[0080] Where I is the input image, K is the convolution kernel, (m, n) is the coordinate of the convolution kernel, K(m, n) is the convolution kernel, (x, y) is the position coordinate in the output feature map, and (I·K)(x, y) represents the pixel value of the output image at position (x, y) after the convolution operation.

[0081] Based on the pooling operation, the maximum pooling method is used to reduce the size of the feature map, reduce the amount of calculation, and retain important feature information. The formula for the maximum pooling operation is:

[0082]

[0083] Among them, P(x, y) represents the pixel value at position (x, y) in the output image after the pooling operation, I(x+m, y+n) represents the pixel value at position (x+m, y+n) in the input image, and max m，n Indicates that the maximum value within the pooling window is taken during the pooling operation.

[0084] Category output unit: Based on recurring objects with specific structures and features, a convolutional neural network is used to train the object recognition model. Through multiple layers of convolution, activation, pooling, and fully connected layers, the spatial features in the image are learned and the object categories in the image are output.

[0085] Object detection and classification unit: detects specific objects in image frames, locates objects, and classifies objects;

[0086] Detect specific objects in the image frame, locate the objects through the output of the model, and locate the position of the objects in the image based on the bounding box coordinates output by the model;

[0087] Perform object classification and identify the object categories in the image. The model outputs a probability distribution, which is the probability that the object in the image belongs to each category, and selects the category with the highest probability as the predicted category of the object.

[0088] Post-processing unit: After the object recognition module completes object detection, the non-maximum suppression method is used to eliminate duplicate detection frames;

[0089] After the object recognition module completes object detection, non-maximum suppression post-processing technology is used to eliminate duplicate detection frames and retain the most likely detection frames to prevent the same object from being recognized multiple times. The algorithm steps of non-maximum suppression are as follows:

[0090] Sort bounding boxes by classification confidence;

[0091] Select the bounding box with the highest confidence as the reference;

[0092] Remove the boxes that have a large overlap with the reference box, where the formula for calculating the intersection of two bounding boxes is:

[0093]

[0094] In the formula, A represents the predicted area, B represents the true area, |A∩B| represents the area of ​​the intersection of A and B, |A∪B| represents the area of ​​the union of A and B, and IOU is the evaluation indicator used to measure the two areas.

[0095] Step 3:

[0096] Output acquisition unit of object recognition module: The object recognition module performs object recognition based on input data, identifies each object in the image and their category and location;

[0097] Object category information: The identified objects are classified into different categories, including "apple", "car" and "chair";

[0098] Position and shape information: The specific position and shape information of the object, including the coordinates of the bounding box and a more precise segmentation mask, will also be passed to the color mapping module;

[0099] Color information matching unit: Based on the category and context information of the object, the color mapping module matches the typical color of the object in the category from the database;

[0100] Object area coloring unit: extracts the area of ​​the object in the image based on the result of object recognition, and defines it by bounding box and segmentation mask;

[0101] Color mapping and repair: Apply the selected color information to the object area for coloring. The color of the object in the image has been partially lost, so repair is performed to restore the typical color of the object. The repair process includes:

[0102] Color filling: If the color of the identified area is lost or inaccurate, the color is repaired through the filling algorithm;

[0103] Model-based color adjustment: If there are changes in lighting, shadows, etc. on the surface of an object, the color mapping module needs to take these factors into account and make appropriate color adjustments;

[0104] Color mapping optimization and fusion: Perform color smoothing, use interpolation to make a smooth transition between the object color and the background color, and perform color correction based on the object's spatial position and lighting information.

[0105] Step 4:

[0106] Object recognition and coloring information acquisition unit: receives the colored object information from the color mapping module, and identifies the area, boundary and missing texture details of the object;

[0107] Image restoration area analysis unit: Determine the edge position of the object based on the edge detection algorithm, identify the object outline, check whether there is any missing texture and details, and use filling and interpolation algorithms to supplement them;

[0108] Based on the edge detection algorithm, the edge position of the object is determined and the outline of the object is identified. The edge detection algorithm formula is:

[0109]

[0110] In the formula, G represents the gradient size at a certain position in the image, G x Represents the horizontal gradient of a certain position in the image, G y It represents the vertical gradient of a certain position in the image, θ represents the gradient direction of a certain position in the image, and atan2 is a mathematical function.

[0111] Detail recovery and texture restoration unit: restores these details based on the texture restoration algorithm, fills the image based on the surrounding pixels, and is used to restore texture and color details, enhance the areas where the texture is missing or blurred, and increase the clarity of the details;

[0112] Color restoration and adjustment unit: local adjustment of color based on the object's ambient lighting, shadow area, and other contextual information, adjusting the global and local color balance of the image;

[0113] Detail enhancement and lighting restoration unit: Perform lighting restoration according to the lighting conditions on the object surface, restore natural lighting effects, and adjust the brightness and contrast of the image through HDR technology.

[0114] In summary, the advantages of the present invention are:

[0115] The system of the present invention can automatically identify objects in images and color and repair them, avoiding the tediousness and inefficiency of the traditional manual repair process. Through the deep learning model, the system can intelligently restore the color and details of the objects according to the characteristics of the objects in the image.

[0116] Through multi-layer convolutional neural networks for feature extraction and object detection, the system can efficiently identify specific objects in images, especially those that appear repeatedly in movies and have specific structures and features. Based on these recognition results, the system can accurately locate and classify objects, providing accurate input for subsequent restoration and coloring.

[0117] The system automatically restores the typical color of the object through the color mapping module, and adjusts the color according to the object's ambient lighting, shadow and other information to ensure a natural visual transition of the restored image. In addition, the restoration processing module can restore the texture and details of the object, using texture restoration and interpolation algorithms to supplement the details, making the surface of the object more refined and the lighting effect more natural;

[0118] The deep learning-based image restoration technology is used to restore the color and details of objects more realistically. Through color mapping optimization and fusion, color smoothing, and HDR technology for brightness and contrast restoration, the restored image visual effect is ensured to be clearer and more delicate, greatly improving the visual quality of old movie images.

[0119] This system can not only meet the needs of most object restoration in movie images, but also handle complex situations such as different lighting conditions and shadow changes. It has strong adaptability and robustness. By introducing deep learning models, especially convolutional neural networks for feature extraction and object recognition, this system can ensure the accuracy of image restoration on the basis of high efficiency. The system can identify and repair key objects in the image, restore its realism and details, and make the movie image close to the original state in visual effect.

[0120] The system of the present invention can be customized and expanded according to the needs of different movies or images, and can process various types of movie images, not only limited to black and white movies or old movies, but also suitable for other image restoration and colorization tasks.

[0121] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A movie image object recognition and automatic color restoration system based on deep learning, characterized in that: include: Image input module: The image input module is used to receive the movie image frame to be restored; Object recognition module: The object recognition module is electrically connected to the image input module. The object recognition module is used to recognize specific objects appearing in the image frame based on a deep learning algorithm. The specific objects are objects with specific structures and features that appear repeatedly in the movie. Color mapping module: The color mapping module is electrically connected to the object recognition module, and is used to perform color restoration on the recognized object through the model according to known object color information; Repair processing module: The repair processing module is electrically connected to the color mapping module, and is used to repair the identified and colored objects based on an automated algorithm to restore their colors and details; Image output module: The image output module is electrically connected to the restoration processing module, and the image output module is used to output the restored movie image frame.

2. The deep learning-based movie image object recognition and automatic color restoration system according to claim 1, characterized in that: The image input module specifically includes: Image acquisition unit: receiving the movie image frames to be restored, the image frames are extracted from the video files and input through the real-time video stream; Preprocessing unit: performs preliminary preprocessing on the received movie images, including denoising, grayscale and contrast adjustment; Frame processing unit: decomposes the movie video into independent image frames, and each frame is analyzed and repaired as an independent processing unit; Size normalization unit: performs size normalization on the input image frames to ensure that all input images have the same resolution and size; Data format conversion unit: According to the requirements of subsequent modules, the input image is converted into a matrix data format and the received image frames are temporarily stored.

3. The deep learning-based movie image object recognition and automatic color restoration system according to claim 2, characterized in that: The object recognition module comprises: Feature extraction unit: Based on the deep learning object recognition model, multiple convolutional layers are used to extract features; Category output unit: Based on recurring objects with specific structures and features, a convolutional neural network is used to train the object recognition model. Through multiple layers of convolution, activation, pooling, and fully connected layers, the spatial features in the image are learned and the object categories in the image are output. Object detection and classification unit: detects specific objects in image frames, locates objects, and classifies objects; Post-processing unit: After the object recognition module completes object detection, the non-maximum suppression method is used to eliminate duplicate detection frames.

4. The deep learning-based movie image object recognition and automatic color restoration system according to claim 3, characterized in that: The feature extraction unit specifically includes: Based on the convolutional neural network, feature extraction is performed through multiple convolutional layers. The convolutional neural network uses the convolution kernel to perform convolution operations on the image to extract the local features of the image, including edges, textures, and colors. The convolution operation formula is: Where I is the input image, K is the convolution kernel, (m, n) is the coordinate of the convolution kernel, K(m, n) is the convolution kernel, (x, y) is the position coordinate in the output feature map, and (I K)(x, y) represents the pixel value of the output image at position (x, y) after the convolution operation. Based on the pooling operation, the maximum pooling method is used to reduce the size of the feature map, reduce the amount of calculation, and retain important feature information. The formula for the maximum pooling operation is: Among them, P(x,y) represents the pixel value at position (x,y) in the output image after the pooling operation, I(x+m,y+n) represents the pixel value at position (x+m,y+n) in the input image, and max m,n Indicates taking the maximum value within the pooling window during the pooling operation.

5. The deep learning-based movie image object recognition and automatic color restoration system according to claim 4, characterized in that: The object detection and classification unit specifically includes: Detect specific objects in the image frame, locate the objects through the output of the model, and locate the position of the objects in the image based on the bounding box coordinates output by the model; Perform object classification and identify the object categories in the image. The model outputs a probability distribution, which is the probability that the object in the image belongs to each category, and selects the category with the highest probability as the predicted category of the object.

6. The deep learning-based movie image object recognition and automatic color restoration system according to claim 5, characterized in that: The post-processing unit specifically includes: After the object recognition module completes object detection, it uses non-maximum suppression post-processing technology to eliminate duplicate detection frames and retain the most likely detection frames to prevent the same object from being recognized multiple times. The formula for calculating the intersection of two bounding boxes is: In the formula, A represents the predicted area, B represents the true area, |A∩B| represents the area of ​​the intersection of A and B, |A∪B| represents the area of ​​the union of A and B, and IOU is the evaluation indicator used to measure the two areas.

7. The deep learning-based movie image object recognition and automatic color restoration system according to claim 6, characterized in that: The color mapping module specifically includes: Output acquisition unit of object recognition module: The object recognition module performs object recognition based on input data, identifies each object in the image and their category and location; Color information matching unit: Based on the category and context information of the object, the color mapping module matches the typical color of the object in the category from the database; Object region coloring unit: Extracts the region of the object in the image based on the result of object recognition and defines it by means of bounding box and segmentation mask.

8. The deep learning-based movie image object recognition and automatic color restoration system according to claim 7, characterized in that: The object area coloring unit specifically includes: Color mapping and restoration: Apply the selected color information to the object area to perform coloring operations. The color of the object in the image has been partially lost, so restoration is performed to restore the typical color of the object; Color mapping optimization and fusion: Perform color smoothing, use interpolation to make a smooth transition between the object color and the background color, and perform color correction based on the object's spatial position and lighting information.

9. The deep learning-based movie image object recognition and automatic color restoration system according to claim 8, characterized in that: The repair processing module specifically includes: Object recognition and coloring information acquisition unit: receives the colored object information from the color mapping module, and identifies the area, boundary and missing texture details of the object; Image restoration area analysis unit: Determine the edge position of the object based on the edge detection algorithm, identify the object outline, check whether there is any missing texture and details, and use filling and interpolation algorithms to supplement them; Detail recovery and texture restoration unit: restores these details based on the texture restoration algorithm, fills the image based on the surrounding pixels, and is used to restore texture and color details, enhance the areas where the texture is missing or blurred, and increase the clarity of the details; Color restoration and adjustment unit: local adjustment of color based on the object's ambient lighting, shadow area, and other contextual information, adjusting the global and local color balance of the image; Detail enhancement and lighting restoration unit: Perform lighting restoration according to the lighting conditions on the object surface, restore natural lighting effects, and adjust the brightness and contrast of the image through HDR technology.

10. The deep learning-based movie image object recognition and automatic color restoration system according to claim 9, characterized in that: The image restoration region analysis unit specifically includes: Based on the edge detection algorithm, the edge position of the object is determined and the outline of the object is identified. The edge detection algorithm formula is: In the formula, G represents the gradient size at a certain position in the image, G x Represents the horizontal gradient of a certain position in the image, G y It represents the vertical gradient of a certain position in the image, θ represents the gradient direction of a certain position in the image, and atan2 is a mathematical function.

Images