CN114792285A - Image processing method and processing device, electronic device and readable storage medium - Google Patents

Abstract

The present application discloses an image processing method and processing device, an electronic device and a readable storage medium. The image processing method includes: locating a target area in a first image to obtain position information of the target area; erasing image information of a preset area in the first image to obtain an erased second image, wherein the second image is The size is the same as that of the first image, and the preset area includes the target area; according to the position information, an area image corresponding to the target area is intercepted in the second image; and a processed target image is generated according to the area image and the first image.

Description

Image processing method and processing device, electronic device and readable storage medium

technical field

The present application belongs to the technical field of image processing, and in particular relates to an image processing method and processing device, an electronic device and a readable storage medium.

Background technique

In the related art, users sometimes have a need to erase or hide specific content in the picture, such as erasing the text in the picture.

Existing processing methods, such as erasing a specific area by means of image restoration, only utilizes the texture information of the erased area, resulting in poor erasing effect and leaving obvious restoration marks on the picture. If you use deep learning to process pictures, you can reduce the repair marks, but it is easy to erase other areas in the picture that are similar to the content of a specific area, and the processing effect is not good.

SUMMARY OF THE INVENTION

The purpose of the embodiments of the present application is to provide an image processing method and processing device, an electronic device and a readable storage medium, which can solve the problem of poor processing effect of erasing image content in the prior art.

In a first aspect, an embodiment of the present application provides an image processing method, including:

Locate the target area in the first image, and obtain the location information of the target area;

By using the image processing model, the image information of the preset area in the first image is erased, and the erased second image is obtained, wherein the size of the second image is the same as that of the first image, and the preset area includes the target area;

According to the position information, intercept the area image corresponding to the target area in the second image;

Based on the area image and the first image, a processed target image is generated.

In a second aspect, an embodiment of the present application provides an image processing apparatus, including:

a positioning module, used to locate the target area in the first image, and obtain the position information of the target area;

The erasing module is used for erasing the image information of the preset area in the first image through the image processing model to obtain the erased second image, wherein the size of the second image is the same as that of the first image, and the preset area includes target area;

an interception module, configured to intercept an area image corresponding to the target area in the second image according to the position information;

The processing module is used for generating the processed target image according to the area image and the first image.

In a third aspect, embodiments of the present application provide an electronic device, including a processor and a memory, where the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the method of the first aspect is implemented step.

In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method of the first aspect are implemented.

In a fifth aspect, an embodiment of the present application provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement the steps of the method of the first aspect .

In a sixth aspect, an embodiment of the present application provides a computer program product, where the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the method according to the first aspect.

In the embodiment of the present application, the image area to be processed is firstly located, and the location information is recorded. Then, through the confrontation model, image processing is performed on the first image as a whole. The image processing steps are based on image recognition technology and deep learning technology. The model automatically identifies the part that needs to be processed, and according to the global information of the first image, it needs to be processed. The preset area is erased, and the image of the erased preset area is kept in harmony with the overall image.

After the erasing is completed, according to the position information of the marked target area, in the second image erased by the image processing model, intercept the area image of the same position, and combine the area image with the original first image , that is, only the part of the second image that the user needs to eliminate is selected to process the original image. Therefore, the final target image, on the condition that the coordination of the overall image is preserved, will not affect the content of the target area that needs to be processed. Erasing is carried out, while ensuring that other similar areas in the image will not be erased by mistake, which improves the processing efficiency when erasing specific content in the image.

Description of drawings

FIG. 1 shows one of the flowcharts of the image processing method according to an embodiment of the present application;

FIG. 2 shows one of the schematic diagrams of an image processing method according to an embodiment of the present application;

FIG. 3 shows the second schematic diagram of an image processing method according to an embodiment of the present application;

FIG. 4 shows a third schematic diagram of an image processing method according to an embodiment of the present application;

FIG. 5 shows a schematic structural diagram of an image processing model according to an embodiment of the present application;

FIG. 6 shows the second flowchart of the image processing method according to the embodiment of the present application;

FIG. 7 shows a structural block diagram of an image processing apparatus according to an embodiment of the present application;

FIG. 8 shows a structural block diagram of an electronic device according to an embodiment of the present application;

FIG. 9 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the protection scope of this application.

The terms "first", "second" and the like in the description and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and distinguish between "first", "second", etc. The objects are usually of one type, and the number of objects is not limited. For example, the first object may be one or more than one. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the associated objects are in an "or" relationship.

The image processing method and processing apparatus, electronic device, and readable storage medium provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.

In some embodiments of the present application, an image processing method is provided. FIG. 1 shows one of the flowcharts of the image processing method according to an embodiment of the present application. As shown in FIG. 1 , the method includes:

Step 102, locate the target area in the first image, and obtain the position information of the target area;

Step 104, through the image processing model, erase the image information of the preset area in the first image to obtain the erased second image;

In step 104, the size of the second image is the same as that of the first image, and the preset area includes the target area;

Step 106, according to the position information, intercept the area image corresponding to the target area in the second image;

Step 108: Generate a processed target image according to the area image and the first image.

In this embodiment of the present application, when the user wishes to hide or erase specific content in the image, for example, when the user wishes to hide the text content in the image, firstly, the text area in the image can be detected by a text recognition algorithm, etc. Perform positioning and record the location information corresponding to the area. The location information may be coordinate information.

After obtaining the location information of the target area to be erased, the entire original image of the first image is input into the preset image processing model. The target information to be erased is automatically identified in an image, and the whole of the first image is processed according to the global information of the target image.

Specifically, FIG. 2 shows one schematic diagram of an image processing method according to an embodiment of the present application. As shown in FIG. 2 , the first image 200 includes text information 202 , and the user needs to erase the text information 202 . First, by means such as optical character recognition (Optical Character Recognition, OCR), in the first image 200, the area where the text information 202 is located, that is, the target area 204, is marked, and the coordinate information of the target area 204 is recorded at the same time, thereby recording Location information of the target area 204 .

After obtaining the position information of the target area, the first image is input into a preset image processing model. The image processing model can automatically identify the content of the image information that the user wants to erase, such as text information, and erase it based on the global information of the target image, including color information, pixel information, and so on.

FIG. 3 shows the second schematic diagram of the image processing method according to the embodiment of the present application. As shown in FIG. 3 , the image processing model processes the whole of the first image 300, and identifies several pre-images that contain text. All areas are erased. As shown in FIG. 3 , the image processing model performs erasing processing on both preset areas, wherein the first preset area 302 contains the text information that the user needs to erase, and the second preset area 304 contains Since the characteristics of the two-dimensional code information and the text information are close, the two-dimensional code information is mistakenly identified as text and erased.

Further, after the image processing model outputs the second image, according to the position information of the identified target area, in the second image, according to the same coordinates, the corresponding area image is cut out, and the area image is based on the coordinates of the target area. , superimposed on the original image of the first image, so as to cover the target area of the first image, thereby generating the target image.

FIG. 4 shows the third schematic diagram of the image processing method according to the embodiment of the present application. As shown in FIG. 4 , on the target image 400 , the text information in the target area 402 is erased, but the two-dimensional code 404 is retained.

In the embodiment of the present application, according to the position information of the marked target area, in the second image erased by the image processing model, the area image of the same position is intercepted, and the area image is combined with the original first image, that is, Only the part of the second image that the user needs to eliminate is selected to process the original image. Therefore, the final target image, under the condition of keeping the coordination of the overall image, is processed on the content of the target area that needs to be processed. Erasing, while ensuring that other similar areas in the image will not be erased by mistake, improving the processing efficiency when erasing specific content in the image.

In some embodiments of the present application, generating the processed target image according to the area image and the first image includes: covering the area image on the target area according to the position information to obtain the target image.

In the embodiment of the present application, after the second image is obtained through the image processing model, an area image corresponding to the position of the target area is cut out from the second image according to the location information of the target area, such as coordinate information.

After obtaining the area image, also according to the coordinate information of the target area, the obtained area image processed by the image processing model is overlaid on the original first image, so that the image content in the target area is completely replaced by area image, so that in the replaced target image, only the area that needs to be erased is replaced by the processed image, and the content of the target area that needs to be processed is erased under the condition that the coordination of the overall image is preserved. At the same time, it is ensured that other similar areas in the image will not be erased by mistake, which improves the processing efficiency when erasing specific content in the image.

In some embodiments of the present application, before erasing the image information of the preset area in the first image, the method further includes:

Obtaining a first training image and a second training image, wherein the second training image is an image obtained after removing preset image information in the first training image;

Through the first training image and the second training image, the preset model is trained to obtain a trained image processing model, and the image processing model includes a first network and a second network;

Erase the image information of the preset area in the first image, including:

Erasing the first image through the first network to obtain a processed third image;

Perform down-sampling processing on the third image to obtain a fourth image;

Erasing the fourth image through the second network to obtain the processed fifth image;

Up-sampling processing is performed on the fifth image to obtain a second image.

In the embodiment of the present application, a preset confrontation model is trained to obtain a trained image processing model. Specifically, the first image is collected first, and the first image is manually processed to generate the second image. The first image is an original image, and the second image is a second image obtained by erasing a preset area such as text content through software such as image editing or image modification.

According to the first image and the second image, as a training set, a preset generative adversarial network is trained to perform end-to-end erasure of the entire image text. The specific method is to build a network model. For the generation network, the network structure of unet is used. First, the image is down-sampled to obtain the semantic information of the image, and then the image is up-sampled and restored to the original size to obtain the image output. The optimization can not get good results, so after the unet (a U-shaped network for dense prediction and segmentation) structure, a layer of lightweight unet results are added for further processing to obtain the final processing results.

Further, a discriminant network is constructed. The discriminant network includes a double-scale network, where the first scale network includes several concatenated convolutions, where the convolution stride can be set to 1, and no pooling layer is introduced to ensure that The resolution of the pictures does not drop.

Then, after the first scale network, add another layer of the same scale network, but the input of the second layer scale network is obtained after downsampling the output of the first layer network. The ground truth of the dual-scale network is obtained from the erased image and the image with double downsampling of the erased image, respectively.

Input the output result of the generation network into the discriminant network, and judge the difference between the currently generated erased image and the original annotated erased image (ie, the second image) through the discriminant network, and use it as a loss (loss) reverse The optimized network parameters are propagated, and finally the optimized network structure is obtained.

In this optimized network structure, the model obtained after removing the discriminant network is the above-mentioned image processing model. FIG. 5 shows a schematic structural diagram of an image processing model according to an embodiment of the present application. As shown in FIG. 5 , the image processing model 500 includes a two-layer unet network structure, namely a first network 502 and a second network 504 .

When using the image processing model 500 to erase specific content in the first image 506 , first, the first image 506 to be processed is input into the first network 502 , and the specific information in the first image 502 is processed through the first network 502 . Erase.

After erasing, the processed third image 508 is obtained, and the third image 508 is down-sampled to obtain a fourth image 510 with a reduced resolution, thereby acquiring the semantic definition of the image, and the down-sampled fourth image 510 is used as the second image 510. Layer network, that is, the input of the second network 504 .

Finally, the fifth image 512 output by the second network 504 is restored to the original size after upsampling to obtain the final second image 514 .

It can be understood that the content contained in the first image is the content that the user needs to erase. If the user needs to erase the text information in the image, the first image contains the text information. If the user needs to erase the face, the first image contains the face information.

The present application trains the image recognition model, so that the image recognition model can erase the corresponding content area in the first image according to the image content in the training set, and can keep the erased image in harmony with the overall image , to improve image processing efficiency.

In some embodiments of the present application, the target area is a character image area, and locating the target area in the first image includes:

performing optical character recognition on the first image, and obtaining a character detection frame in the first image;

The target area is located in the first image according to the coordinate information of the character detection frame.

In this embodiment of the present application, the target area specifically includes a character image area, that is, the user needs to erase the character area in the first image. Specifically, optical character (OCR) recognition is first performed on the first image, so that in the first image, the positions of characters are detected, and at the same time, a character detection frame is formed according to the positions of these characters.

Specifically, the first image is preprocessed first, and in some embodiments, a denoising algorithm is used to remove noise on the first image. After that, according to the trained OCR detection model, the text or characters are obtained through the OCR detection algorithm, and the coordinate information is located. The OCR detection algorithm can obtain the character detection frame in various scenarios such as horizontal, vertical, and curved.

For the horizontal and vertical regular character detection frame, it can be represented by a four-point coordinate frame; while for the curved irregular character detection frame, it can be represented by an eight-point coordinate frame. If there is no text information or character information in the current picture, an empty character coordinate box is returned.

The character detection frame is marked with coordinate information, and the coordinate information refers to the coordinates of the character detection frame in the first image. According to the coordinate information of the character detection frame, the target area is located in the first image, so that after obtaining the second image through the image processing model, the processed area image is covered on the target area according to the coordinate information, so that the generated image In the target image, it not only ensures the coordination of the overall image, but also effectively erases the content of the target area that needs to be processed. Content processing efficiency.

In some embodiments of the present application, FIG. 6 shows the second flowchart of an image processing method according to an embodiment of the present application. As shown in FIG. 6 , the method includes:

Step 602, performing text positioning on the original picture, and recording the text coordinate information in the original picture;

In step 602, the image is first preprocessed, and a denoising algorithm is used to remove noise on the image. After that, the ocr detection model is trained, and the text positioning information is obtained through the ocr detection algorithm. The ocr detection algorithm can obtain the text detection frame in various scenarios such as horizontal, vertical, and curved. The horizontal and vertical text boxes are represented by a four-point coordinate box, and the curved text box is represented by an eight-point coordinate box. If there is no text information in the current image, an empty text coordinate box is returned.

Step 604, collect paired data, and train a generative adversarial network for word erasure;

In step 604, a pair of <original pictures, text-erased pictures> is collected, and a generative confrontation method is used for model training to obtain a picture processing model.

Specifically, the original picture is first collected and obtained, and the corresponding text-erased picture is obtained through PS, so as to obtain pairs of <original picture, text-erased picture>.

Then train a generative adversarial network for end-to-end erasure of the whole image text; the specific method is to build a network model, use the unet network structure for the generation network, first downsample the image to obtain image semantic information, and then upsample the image Return to the original size to obtain the image output. Considering that the optimization of the model for complex scenes cannot get good results, a layer of lightweight unet is added after the unet structure for further processing to obtain the final processing result. Build a discriminant network. The discriminant network consists of a double-scale network. The first scale network consists of several convolution cascades. The step size of the convolution is set to 1, and the pooling layer is not introduced to ensure the resolution of the image. Do not drop, and then add a layer of the same scale network after the first scale network, but the input of the second layer scale network is obtained by downsampling the output of the first layer network. The ground truth of the dual-scale network is obtained from the erased image and the image with double downsampling of the erased image, respectively.

The output of the generation network is input to the discriminant network to judge the difference between the currently generated erased image and the original annotated erased image, and it is used as the loss backpropagation to optimize the network parameters, and finally the optimized network structure is obtained.

Step 606, using the optimized model to infer the original picture to obtain the erased picture result;

In step 606, use the optimized generative adversarial model to infer the original picture to obtain the erased picture result. The specific method is to input the original picture into the trained model. At this time, the discriminator needs to be removed, and only the generator is retained. That is, the output image obtained after passing through the generator is the image result after erasing.

Step 608: Map the obtained text coordinate information to the erased picture, and cut out the erased area where the text coordinates are located;

In step 608, the obtained text coordinate information is mapped to the obtained erased picture result, and the erased area where the text coordinates are located is cut out; , flowers, etc. are easily erased as text, so in order to ensure that these information are not mispainted, it is necessary to map the text coordinate information recorded from the original image to the acquired erased image, because the coordinates are non-rectangular frames, so it is necessary to Set a mask image as large as the input image. The original mask image is pure black, and the area where the coordinate box is located is set to white, and the erased area where the text coordinates to be cropped can be obtained.

Step 610: Paste the erased area back to the original image to obtain the final text erased image.

In step 610, the cropped erased area is pasted back to the original image to obtain the final text erased image.

The specific method is that when the mask image corresponding to the text area that needs to be erased has been obtained, you only need to paste the erase area corresponding to the pure white area in the mask image back to the original image, so that the final image can be obtained. The required text erases the picture, thereby avoiding mispainting of areas that are very similar to the text.

In some embodiments of the present application, an image processing apparatus is provided. FIG. 7 shows a structural block diagram of an image processing apparatus according to an embodiment of the present application. As shown in FIG. 7 , the image processing apparatus 700 includes:

A positioning module 702, configured to locate the target area in the first image, and obtain the position information of the target area;

The erasing module 704 is used for erasing the image information of the preset area in the first image through the image processing model to obtain the erased second image, wherein the size of the second image is the same as that of the first image, and the preset area is the same as the first image. including the target area;

The interception module 706 is used for intercepting the area image corresponding to the target area in the second image according to the position information;

The processing module 708 is configured to generate a processed target image according to the area image and the first image.

In the embodiment of the present application, according to the position information of the marked target area, in the second image erased by the image processing model, the area image of the same position is intercepted, and the area image is combined with the original first image, that is, Only the part of the second image that the user needs to eliminate is selected to process the original image. Therefore, the final target image, under the condition of keeping the coordination of the overall image, is processed on the content of the target area that needs to be processed. Erasing, while ensuring that other similar areas in the image will not be erased by mistake, improving the processing efficiency when erasing specific content in the image.

In some embodiments of the present application, the image processing apparatus further includes: an overlay module, configured to overlay the area image on the target area according to the location information to obtain the target image.

After the region image is obtained in the embodiment of the present application, the captured region image processed by the image processing model is also overlaid on the original first image according to the coordinate information of the target region, so that the content of the image in the target region is changed. , completely replaced with the area image, so that in the replaced target image, only the area that needs to be erased is replaced by the processed image. Under the condition that the coordination of the overall image is preserved, the target area that needs to be processed The content is erased, while ensuring that other similar areas in the image will not be erased by mistake, which improves the processing efficiency when erasing specific content in the image.

In some embodiments of the present application, the processing device further includes:

an acquisition module for acquiring a first training image and a second training image, wherein the second training image is an image obtained after removing preset image information in the first training image;

a training module for training a preset model through the first training image and the second training image to obtain a trained image processing model, the image processing model including a first network and a second network;

The erasing module is also used for erasing the first image through the first network to obtain the processed third image;

a sampling module for down-sampling the third image to obtain a fourth image;

The erasing module is also used for erasing the fourth image through the second network to obtain the processed fifth image;

The sampling module is further configured to perform up-sampling processing on the fifth image to obtain the second image.

In the present application, the image recognition model is trained, so that the image recognition model can erase the corresponding content area in the first image according to the image content in the training set, and the erased image can keep the coordination of the overall image. , to improve image processing efficiency.

In some embodiments of the present application, the target area is a character image area, and the processing device further includes:

a recognition module for performing optical character recognition on the first image, and obtaining a character detection frame in the first image;

The positioning module is further configured to locate the target area in the first image according to the coordinate information of the character detection frame.

In this embodiment of the present application, the target area is located in the first image, so that after obtaining the second image through the image processing model, the processed area image is covered on the target area according to the coordinate information, so that in the generated target image, It not only ensures the coordination of the overall image, but also effectively erases the content of the target area that needs to be processed. At the same time, it ensures that other similar areas in the image will not be erased by mistake, which improves the processing of erasing specific content in the image. efficiency.

The image processing apparatus in this embodiment of the present application may be an electronic device, or may be a component in the electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices other than the terminal. Exemplarily, the electronic device may be a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a vehicle-mounted electronic device, a Mobile Internet Device (MID), an augmented reality (AR)/virtual reality (VR) Devices, robots, wearable devices, ultra-mobile personal computers (UMPCs), netbooks or personal digital assistants (PDAs), etc., and can also be servers, network attached storages (NAS) , a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which are not specifically limited in the embodiments of the present application.

The image processing apparatus in this embodiment of the present application may be an apparatus having an operating system. The operating system may be an Android (Android) operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

The image processing apparatus provided in this embodiment of the present application can implement each process implemented by the foregoing method embodiment, and to avoid repetition, details are not repeated here.

Optionally, an embodiment of the present application further provides an electronic device. FIG. 8 shows a structural block diagram of an electronic device according to an embodiment of the present application. As shown in FIG. 8 , the electronic device 800 includes a processor 802, a memory 804, a storage A program or instruction that is on the memory 804 and can be run on the processor 802, when the program or instruction is executed by the processor 802, realizes the various processes of the above method embodiments, and can achieve the same technical effect. In order to avoid repetition, I won't go into details here.

It should be noted that the electronic devices in the embodiments of the present application include the aforementioned mobile electronic devices and non-mobile electronic devices.

FIG. 9 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.

The electronic device 900 includes but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909, a processor 910 and other components .

Those skilled in the art can understand that the electronic device 900 may also include a power supply (such as a battery) for supplying power to various components, and the power supply may be logically connected to the processor 910 through a power management system, so that the power management system can manage charging, discharging, and power management. consumption management and other functions. The structure of the electronic device shown in FIG. 9 does not constitute a limitation to the electronic device. The electronic device may include more or less components than the one shown, or combine some components, or arrange different components, which will not be repeated here. .

Wherein, the processor 910 is used to locate the target area in the first image, and obtain the position information of the target area;

By using the image processing model, the image information of the preset area in the first image is erased, and the erased second image is obtained, wherein the size of the second image is the same as that of the first image, and the preset area includes the target area;

According to the position information, intercept the area image corresponding to the target area in the second image;

Based on the area image and the first image, a processed target image is generated.

In the embodiment of the present application, according to the position information of the marked target area, in the second image erased by the image processing model, the area image of the same position is intercepted, and the area image is combined with the original first image, that is, Only the part of the second image that the user needs to eliminate is selected to process the original image. Therefore, the final target image, under the condition of keeping the coordination of the overall image, is processed on the content of the target area that needs to be processed. Erasing, while ensuring that other similar areas in the image will not be erased by mistake, improving the processing efficiency when erasing specific content in the image.

Optionally, the processor 910 is further configured to overlay the area image on the target area according to the location information to obtain the target image.

After the region image is obtained in the embodiment of the present application, the captured region image processed by the image processing model is also overlaid on the original first image according to the coordinate information of the target region, so that the content of the image in the target region is changed. , completely replaced with the area image, so that in the replaced target image, only the area that needs to be erased is replaced by the processed image. Under the condition that the coordination of the overall image is preserved, the target area that needs to be processed The content is erased, while ensuring that other similar areas in the image will not be erased by mistake, which improves the processing efficiency when erasing specific content in the image.

Optionally, the processor 910 is further configured to acquire a first training image and a second training image, wherein the second training image is image data obtained after erasing a preset area in the first training image;

Through the first training image and the second training image, the preset model is trained to obtain a trained image processing model, and the image processing model includes a first network and a second network;

Erase the image information of the preset area in the first image, including:

Erasing the first image through the first network to obtain a processed third image;

Perform down-sampling processing on the third image to obtain a fourth image;

Erasing the fourth image through the second network to obtain the processed fifth image;

Up-sampling processing is performed on the fifth image to obtain a second image.

In the present application, the image recognition model is trained, so that the image recognition model can erase the corresponding content area in the first image according to the image content in the training set, and the erased image can keep the coordination of the overall image. , to improve image processing efficiency.

Optionally, the target area is a character image area, and the processor 910 is further configured to perform optical character recognition on the first image, and obtain a character detection frame in the first image;

The target area is located in the first image according to the coordinate information of the character detection frame.

In this embodiment of the present application, the target area is located in the first image, so that after obtaining the second image through the image processing model, the processed area image is covered on the target area according to the coordinate information, so that in the generated target image, It not only ensures the coordination of the overall image, but also effectively erases the content of the target area that needs to be processed. At the same time, it ensures that other similar areas in the image will not be erased by mistake, which improves the processing of erasing specific content in the image. efficiency.

It should be understood that, in this embodiment of the present application, the input unit 904 may include a graphics processor (Graphics Processing Unit, GPU) 9041 and a microphone 9042. camera) to process the image data of still pictures or videos. The display unit 906 may include a display panel 9061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 907 includes at least one of a touch panel 9071 and other input devices 9072 . The touch panel 9071 is also called a touch screen. The touch panel 9071 may include two parts, a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be repeated here.

The memory 909 may be used to store software programs as well as various data. The memory 909 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, image playback function, etc.), etc. Additionally, memory 909 may include volatile memory or non-volatile memory, or memory 909 may include both volatile and non-volatile memory. Wherein, the non-volatile memory may be Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (Erasable PROM, EPROM), Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous random access memory) DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). The memory 909 in this embodiment of the present application includes, but is not limited to, these and any other suitable types of memory.

The processor 910 may include one or more processing units; optionally, the processor 910 integrates an application processor and a modem processor, wherein the application processor mainly processes operations involving an operating system, a user interface, and an application program, etc. Modem processors mainly deal with wireless communication signals, such as baseband processors. It can be understood that, the above-mentioned modulation and demodulation processor may not be integrated into the processor 910.

Embodiments of the present application further provide a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, each process of the foregoing method embodiments can be implemented, and the same technology can be achieved The effect, in order to avoid repetition, is not repeated here.

Wherein, the processor is the processor in the electronic device described in the foregoing embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.

An embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement each process of the foregoing method embodiments , and can achieve the same technical effect, in order to avoid repetition, it is not repeated here.

It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip, or the like.

The embodiments of the present application provide a computer program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the various processes in the above method embodiments, and can achieve the same technical effect, which is To avoid repetition, we will not repeat them here.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in the reverse order depending on the functions involved. To perform functions, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to some examples may be combined in other examples.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present application can be embodied in the form of computer software products, which are essentially or contribute to the prior art. The computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of this application, without departing from the scope of protection of the purpose of this application and the claims, many forms can be made, which all fall within the protection of this application.

Claims (10)

1. an image processing method, is characterized in that, comprises: Locate the target area in the first image, and obtain the position information of the target area; Erasing image information of a preset area in the first image to obtain an erased second image, wherein the size of the second image is the same as the size of the first image, and the preset area includes all the target area; According to the position information, intercept an area image corresponding to the target area in the second image; Based on the area image and the first image, a processed target image is generated. 2. The image processing method according to claim 1, wherein the generating a processed target image according to the region image and the first image comprises: According to the position information, the area image is covered on the target area to obtain the target image. 3. The image processing method according to claim 1, wherein before the erasing the image information of the preset area in the first image, the method further comprises: Obtain a first training image and a second training image, wherein the first training image contains preset image information, and the second training image is in the first training image after removing the preset image information the resulting image; By training the preset model through the first training image and the second training image, a trained image processing model is obtained, and the image processing model includes a first network and a second network; The erasing of the image information of the preset area in the first image includes: Erasing the first image through the first network to obtain a processed third image; performing down-sampling processing on the third image to obtain a fourth image; Erasing the fourth image through the second network to obtain a processed fifth image; Up-sampling processing is performed on the fifth image to obtain the second image. 4. The image processing method according to any one of claims 1 to 3, wherein the target area is a character image area, and the locating the target area in the first image comprises: performing optical character recognition on the first image, and obtaining a character detection frame in the first image; The target area is located in the first image according to the coordinate information of the character detection frame. 5. An image processing device, comprising: a positioning module, configured to locate a target area in the first image to obtain position information of the target area; The erasing module is used for erasing the image information of the preset area in the first image to obtain an erased second image, wherein the size of the second image is the same as the size of the first image, so the preset area includes the target area; an interception module, configured to intercept an area image corresponding to the target area in the second image according to the position information; A processing module, configured to generate a processed target image according to the area image and the first image. 6. The image processing device according to claim 5, further comprising: An overlay module, configured to overlay the area image on the target area according to the location information to obtain the target image. 7. The image processing device according to claim 5, further comprising: an acquisition module, configured to acquire a first training image and a second training image, wherein the second training image is image data obtained after erasing the preset area in the first training image; a training module, configured to train a preset model through the first training image and the second training image to obtain the trained image processing model, where the image processing model includes a first network and a second network; The erasing module is further configured to perform erasing processing on the first image through the first network to obtain a processed third image; a sampling module for downsampling the third image to obtain a fourth image; The erasing module is further configured to perform erasing processing on the fourth image through the second network to obtain a processed fifth image; The sampling module is further configured to perform up-sampling processing on the fifth image to obtain the second image. 8. The image processing apparatus according to any one of claims 5 to 7, wherein the target area is a character image area, and the processing apparatus further comprises: a recognition module, configured to perform optical character recognition on the first image, and obtain a character detection frame in the first image; The positioning module is further configured to locate the target area in the first image according to the coordinate information of the character detection frame. 9. An electronic device, characterized in that it comprises a processor and a memory, wherein the memory stores a program or an instruction that can be run on the processor, and when the program or instruction is executed by the processor, the implementation as claimed in the claims Steps of the image processing method described in any one of 1 to 4. 10. A readable storage medium, wherein a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the image according to any one of claims 1 to 4 is realized The steps of the processing method.

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