CN108961156B - Method and device for processing face image - Google Patents

Abstract

The present disclosure relates to a method and device for face image processing, and belongs to the field of electronic technology applications. The method includes: performing the first dermabrasion treatment on the initial face image to obtain a basic image; based on the initial face image, obtaining a detail image, the detail image being used to reflect the texture characteristics of the skin in the initial face image; The image is subjected to the second microdermabrasion process to obtain the target detail image; the target detail image is superimposed on the basic image to obtain the target face image. The present disclosure can solve the problem of lack of texture due to the lack of necessary texture features in the skin of the processed human face image during the processing of the human face image. This disclosure is used for the processing of face images.

Description

Method and device for face image processing

technical field

The present disclosure relates to the field of image processing, in particular to a method and device for face image processing.

Background technique

In the process of beautifying facial images, face microdermabrasion is the core function of beautifying technology. It can process impurities, textures and other details on the skin to make the picture look more beautiful.

At present, the face microdermabrasion technology is generally implemented by using an edge-preserving filter. In the process of processing the face image, the edge-preserving filter will filter the initial face image to be processed, so as to obtain the face after the microdermabrasion treatment. face image.

However, when using an edge-preserving filter to perform skin smoothing on a human face image, the texture features of the skin in the human face image will be smoothed, resulting in a lack of texture in the skin after skin grinding.

Contents of the invention

Embodiments of the present disclosure provide a method and device for processing a human face image. Can solve the problem of prior art. Described technical scheme is as follows:

According to a first aspect of an embodiment of the present disclosure, a method for processing a human face image is provided, including:

Carrying out the first microdermabrasion treatment on the initial face image to obtain the basic image;

Acquiring a detail image based on the initial human face image, the detailed image is used to reflect the texture characteristics of the skin in the initial human face image;

performing a second skin smoothing process on the detail image to obtain a target detail image;

The target detail image is superimposed on the basic image to obtain a target face image.

Optionally, the acquiring a detail image based on the initial face image includes:

The difference between the initial face image and the basic image is obtained to obtain the detail image.

Optionally, the second microdermabrasion processing is performed on the detail image to obtain the target detail image, including:

performing nonlinear transformation on the detail image to obtain a transformed detail image;

performing the second skin smoothing process on the transformed detail image to obtain the target detail image.

Optionally, performing nonlinear transformation on the detail image to obtain the transformed detail image includes:

Based on a polynomial transformation formula, the detail image is nonlinearly transformed to obtain a transformed detail image, and the polynomial transformation formula is:

Wherein, D1 is the i-th pixel value of the transformed detail image, the D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n , n is the total number of pixel values in the detail image.

Optionally, both the first microdermabrasion treatment and the second microdermabrasion treatment are edge-preserving filtering treatments;

Wherein, the first microdermabrasion treatment is bilateral filtering treatment, guided filtering treatment or weighted least squares treatment;

The second microdermabrasion processing is bilateral filtering processing, guided filtering processing or weighted least square processing.

Optionally, the method also includes:

Capture images through camera components;

Perform face recognition on the collected images;

When there is a human face image in the collected image, an image in a predetermined area including the human face image in the collected image is determined as an initial human face image.

According to a second aspect of an embodiment of the present disclosure, a device for processing a face image is provided, including:

The first microdermabrasion module is configured to perform a first microdermabrasion process on the initial face image to obtain a basic image;

An acquisition module configured to acquire a detail image based on the initial face image, and the detail image is used to reflect texture features of the skin in the initial face image;

The second microdermabrasion module is configured to perform a second microdermabrasion process on the detail image to obtain a target detail image;

The overlay module is configured to overlay the target detail image and the base image to obtain a target face image.

Optionally, the acquisition module is configured as:

The difference between the initial face image and the basic image is obtained to obtain the detail image.

Optionally, the second microdermabrasion module includes:

a transformation sub-module configured to perform nonlinear transformation on the detail image to obtain a transformed detail image;

The skin smoothing submodule is configured to perform the second skin smoothing process on the transformed detail image to obtain the target detail image.

Optionally, the transformation submodule is configured as:

Based on a polynomial transformation formula, the detail image is nonlinearly transformed to obtain a transformed detail image, and the polynomial transformation formula is:

Wherein, D1 is the i-th pixel value of the transformed detail image, the D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n , n is the total number of pixel values in the detail image.

Optionally, both the first skin smoothing treatment and the second skin smoothing treatment are edge-preserving filtering treatments.

Wherein, the first microdermabrasion treatment is bilateral filtering treatment, guided filtering treatment or weighted least squares treatment;

The second microdermabrasion processing is bilateral filtering processing, guided filtering processing or weighted least square processing.

Optionally, the device also includes:

The acquisition module is configured to acquire images through the camera component;

A recognition module configured to perform face recognition on the collected images;

The determining module is configured to determine an image in a predetermined area including a human face image in the collected image as an initial human face image when there is a human face image in the collected image.

According to a third aspect of an embodiment of the present disclosure, there is provided a processing device for a face image, the device comprising:

Processing components;

memory for storing executable instructions of said processing component;

Wherein, the processing component is configured to execute any one of the face image processing methods described in the first aspect.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, the computer-readable storage medium stores instructions, and when the readable storage medium is run on a processing component, the processing component executes the first On the one hand, any one of the methods for processing human face images.

The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

The face image processing method and device provided by the embodiments of the present disclosure can obtain a detail image that can reflect the texture characteristics of the human face skin after performing skin smoothing on the initial face image to obtain the basic image. The target face image can be obtained by superimposing the detail image after the skin smoothing process with the basic image. The skin in the target face image effectively retains its texture features on the basis of less impurities and increases the texture of the skin.

It is to be understood that both the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure.

Description of drawings

In order to illustrate the embodiments of the present disclosure more clearly, the accompanying drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. Ordinary technicians can also obtain other drawings based on these drawings on the premise of not paying creative work.

Fig. 1 is a schematic diagram showing an initial human face image according to an exemplary embodiment;

Fig. 2 is a schematic diagram showing a basic image according to an exemplary embodiment;

Fig. 3 is a flowchart of a face image processing method shown according to an exemplary embodiment;

Fig. 4 is a flowchart of another face image processing method shown according to an exemplary embodiment;

Fig. 5 is a flow chart of another face image processing method shown according to an exemplary embodiment;

Fig. 6 is a schematic diagram of a detail image according to an exemplary embodiment;

Fig. 7 is a flow chart of another face image processing method shown according to an exemplary embodiment;

Fig. 8 is a schematic diagram of a target detail image according to an exemplary embodiment;

Fig. 9 is a schematic diagram of a target face image according to an exemplary embodiment;

Fig. 10 is a schematic diagram of an initial face image according to another exemplary embodiment;

Fig. 11 shows intentions of a target face image according to another exemplary embodiment;

Fig. 12 is a block diagram of a device for processing a human face image according to an exemplary embodiment;

Fig. 13 is a block diagram of another device for processing human face images according to an exemplary embodiment;

Fig. 14 is a block diagram of another face image processing device according to an exemplary embodiment;

Fig. 15 is a block diagram of another device for processing a human face image according to an exemplary embodiment;

Fig. 16 is a block diagram of an apparatus for processing a face image according to an exemplary embodiment.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present disclosure, not all of them. . Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present disclosure.

With the rapid development of image processing technology, more and more people will beautify the face image when taking photos or video chatting. Face resurfacing is the core function of beautification technology. The microdermabrasion technology is generally realized by using an edge-preserving filter. The edge-preserving filter is the core device for realizing the function of the face microdermabrasion technology in the terminal. In the process of processing the face image, the initial face image to be processed Skin smoothing is performed to obtain the skin-brushed face image. However, when the edge-preserving filter is used to smooth the skin texture of the face image, the texture of the skin in the face image will be smoothed, resulting in Treated skin lacks texture due to missing essential textural features. Please refer to FIG. 1 and FIG. 2. FIG. 1 is a schematic diagram of an initial human face image acquired by a terminal, and FIG. 2 is a schematic diagram of an image after skin smoothing is performed on the initial human face image by an edge-preserving filter. Figure 2 removes some texture features of the skin on the basis of removing the impurities of the initial face image in Figure 1.

An embodiment of the present disclosure provides a face image processing method that can solve the above problems. As shown in FIG. 3, the method is applied to a terminal, including:

Step 301. Perform a first skin smoothing process on the initial face image to obtain a basic image.

Step 302, based on the initial face image, obtain a detail image, the detail image is used to reflect the texture features of the skin in the initial face image.

Step 303, performing a second skin smoothing process on the detail image to obtain the target detail image.

Step 304, superimposing the target detail image and the base image to obtain the target face image.

To sum up, the face image processing method provided by the embodiment of the present disclosure can obtain a detail image that can reflect the texture characteristics of the face skin after performing skin smoothing on the initial face image to obtain the basic image. Microdermabrasion processing, and superimposing the detail image after microdermabrasion processing with the basic image, the target face image can be obtained. The skin in the target face image effectively retains its texture features on the basis of less impurities. Increases the texture of the skin.

An embodiment of the present disclosure provides a face image processing method, which is applied to a terminal. For example, the terminal may be a mobile phone, and the terminal may be installed with an image processing program, and the image processing program is used to execute a subsequent face image processing method. As shown in Figure 4, the method includes:

Step 401, acquiring an initial face image.

In the embodiment of the present disclosure, there are many ways for the terminal to obtain the initial face image. For example, the user can select the initial face image from locally stored images. Correspondingly, the terminal obtains the initial face image based on the user's selection operation. image; as another example, the user may perform a shooting trigger operation, and correspondingly, after detecting the shooting trigger operation, the terminal turns on the shooting component, and obtains an initial face image through the shooting component. For example, the shooting component is a front camera or a rear camera.

It should be noted that the image captured by the terminal through the photographing component may not include a face image, and the face image processing method provided in this disclosure is aimed at a face image. If the captured image does not include a face image, the corresponding Processing is invalid processing. In order to reduce invalid processing, the terminal can identify the captured image, as shown in Figure 5, the process may include:

Step 4011, collect images through camera components.

For example, the user may perform a shooting trigger operation. Correspondingly, after detecting the shooting trigger operation, the terminal turns on the shooting component, and obtains an image through the shooting component.

Step 4012, perform face recognition on the collected images.

The terminal may perform face recognition on the collected images through a face recognition algorithm (also called a portrait recognition algorithm or a face recognition algorithm).

Step 4013, when there is a human face image in the collected image, determine the image in the predetermined area including the human face image in the collected image as the initial human face image.

When there is a face image in the image collected by the terminal, the image in the predetermined area including the face image in the collected image is determined as the initial face image, and when there is no face image in the collected image, the user can be prompted not to A face image is collected, or the above steps 4011 to 4013 are repeated.

For example, assuming that the image collected in step 4011 is the image in the terminal as shown in Figure 1, and it can be recognized through step 4012 that there is a face image in the image, then the terminal includes the image in the image in the predetermined area of the face image Determined as the initial face image. For example, the predetermined area may be a central area of the captured image, and the area has a specified shape, and the specified shape may be a rectangle, a circle, an ellipse, or the like.

Step 402, performing a first skin smoothing process on the initial face image to obtain a basic image.

Taking the initial human face image as shown in the terminal in FIG. 1 as an example, the first skin smoothing process is performed on it, and the obtained basic image may be the human face image shown in the terminal in FIG. 2 . The first skin smoothing process may be an edge-preserving filtering process, for example, a bilateral filtering process, a guided filtering process, or a weighted least squares process.

Step 403, based on the initial face image, obtain a detail image, the detail image is used to reflect the texture features of the skin in the initial face image.

Exemplarily, the texture feature may be features such as fine lines at the corners of the eyes, pores, nasolabial folds, lips, and/or nasal grooves.

In the embodiment of the present disclosure, based on the initial face image, there may be multiple ways to obtain the detail image. For example, in an optional implementation, the features are extracted from the initial face image texture features to obtain detailed images. In another optional implementation manner, the initial face image can be differentiated from the basic image to obtain the detail image. That is: H=A-B, where A is the pixel value matrix of the initial face image, B is the pixel value matrix of the basic image, and H is the pixel value matrix of the detail image.

Taking the initial face image as the face image shown in the terminal in Figure 1 as an example, the basic image obtained by performing the first dermabrasion treatment on it can be as shown in Figure 2, and the initial face image in Figure 1 and the image in Figure 2 The difference between the base image and the detail image can be obtained as shown in the terminal in Figure 6.

Step 404, performing a second skin smoothing process on the detail image to obtain the target detail image.

In the embodiment of the present disclosure, there may be various processes for performing the second skin smoothing process on the detail image to obtain the target detail image. For example, as shown in FIG. 7 , the process includes:

Step 4041, perform nonlinear transformation on the detail image to obtain the transformed detail image.

For example, based on the polynomial transformation formula, the detail image is nonlinearly transformed to obtain the transformed detail image. The polynomial transformation formula is:

Among them, D1 is the i-th pixel value of the transformed detail image, D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n, n is the The total number of pixel values. For each pixel value of the transformed detail image, the above polynomial change formula is satisfied. For example, a=1024, b=512.

It should be noted that the degree of the polynomial change formula provided by the embodiment of the present disclosure is greater than or equal to 2. The above polynomial change formula is only a schematic illustration, and it can also have other forms. For example, the formula can be a polynomial of degree 3 or polynomial of degree 4.

The nonlinear transformation of the detail image can activate the useful information in the detail image, suppress the irrelevant information in it, improve the efficiency of subsequent processing, and further improve the quality of the final processed image.

Step 4042: Perform a second skin smoothing process on the transformed detail image to obtain the target detail image.

Please refer to the above step 403. Although the detail image reflects the texture feature of the skin in the initial face image, the texture feature is close to the real face state, which is rough and not soft enough. The detail image after step 4041 transformation still has corresponding Problem, in the embodiment of the present disclosure, by performing a second skin smoothing process on the transformed detail image, the texture features reflected in the detail image can be softened to make it more visually attractive. The second skin smoothing process may be an edge-preserving filtering process. For example, bilateral filtering processing, guided filtering processing or weighted least squares processing.

For example, assuming that the face image displayed by the terminal in FIG. 6 is a detail image after nonlinear transformation, the target detail image obtained by performing the second skin smoothing process on it may be the image in the terminal as shown in FIG. 8 .

Step 405, superimposing the target detail image and the basic image to obtain the target face image.

Optionally, the target detail image and the base image can be superimposed to obtain the target face image, that is, E=F+B, where F is the pixel value matrix of the target detail image, and B is the pixel value of the base image Matrix, E is the pixel value matrix of the target face image.

Taking the target detail image as shown in FIG. 8 and the basic image as shown in FIG. 2 , superimposing the two, the obtained target face image can be the face image shown in the terminal in FIG. 9 .

It should be noted that there may be other acquisition methods for the target face image. For example, at least one of the target detail image and the basic image may be subjected to a preset transformation, and then superimposed. Do limit.

Step 406, displaying the target face image.

Optionally, the terminal can display the initial face image and the target face image together on the user interface in the form of comparison, so that the user can effectively see the effect of image processing. For example, the initial face image and the target face image The images are displayed in two areas of the user interface, such as the upper half area and the lower half area of the user interface; optionally, the terminal can superimpose the initial face image and the target face image Commonly displayed on the user interface, so that users can see the effect of image processing in a relatively novel way, for example, superimposing the target face image on the initial face image in a semi-transparent form; Optionally, the terminal can also directly Display the target face image on the user interface. The above several optional manners are only schematic illustrations of the present disclosure, and the present disclosure does not limit it.

Taking the initial face image as the image shown in the terminal in Figure 10 as an example, after the face image skinning process from step 401 to step 405, the target face image displayed on the terminal is shown in Figure 11, the target person The skin in the face image retains the texture features of the face on the basis of less impurities, such as fine lines at the corners of the eyes and lip and nose grooves, which increases the texture of the skin.

To sum up, in the face image processing method provided by the embodiments of the present disclosure, the terminal collects images through the camera component, and after face recognition is performed on the collected images, the collected images include the predetermined area of the face image. The image of the original face is determined as the initial face image, and then the initial face image is subjected to the first dermabrasion treatment to obtain the basic image, and then the detail image that can reflect the texture characteristics of the skin in the initial face image is obtained, and the detail image is processed. The second dermabrasion process is to obtain the target detail image. Finally, the target detail image is superimposed on the basic image to obtain the target face image. The skin in the target face image is effectively preserved on the basis of less impurities. Its textural characteristics add to the texture of the skin.

The present disclosure provides a face image processing device 50, as shown in Figure 12, the device 50 includes:

The first microdermabrasion module 501 is configured to perform a first microdermabrasion process on the initial face image to obtain a basic image;

The obtaining module 502 is configured to obtain a detail image based on the initial face image, and the detail image is used to reflect the texture characteristics of the skin in the initial face image;

The second microdermabrasion module 503 is configured to perform a second microdermabrasion process on the detail image to obtain a target detail image;

The superimposing module 504 is configured to superimpose the target detail image and the basic image to obtain a target human face image.

To sum up, in the face image processing device provided by the embodiments of the present disclosure, the first skin-refining module can perform the first skin-refining process on the initial face image to obtain the basic image, and the acquisition module acquires the basic image based on the initial face image, which can reflect For the detail image of the texture feature of the skin in the initial face image, the second microdermabrasion module can perform a second microdermabrasion process on the detail image to obtain the target detail image, and the superposition module can combine the target detail image with the target detail image The above-mentioned basic image is superimposed to obtain the target face image, and the skin in the obtained target face image has less impurities, effectively retains the texture characteristics of the skin, and increases the texture of the skin.

Optionally, the obtaining module 502 is configured to:

The difference between the initial face image and the basic image is obtained to obtain the detail image.

Optionally, the second microdermabrasion module 503, as shown in Figure 13, the second microdermabrasion module 503 includes:

The transformation sub-module 5031 is configured to perform nonlinear transformation on the detail image to obtain the transformed detail image;

The skin smoothing sub-module 5032 is configured to perform the second skin smoothing process on the transformed detail image to obtain the target detail image.

Optionally, the transformation submodule 5031 is configured to:

Based on a polynomial transformation formula, the detail image is nonlinearly transformed to obtain a transformed detail image, and the polynomial transformation formula is:

Wherein, D1 is the i-th pixel value of the transformed detail image, the D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n , n is the total number of pixel values in the detail image.

Optionally, a=1024, b=512.

Optionally, both the first skin smoothing treatment and the second skin smoothing treatment are edge-preserving filtering treatments.

Optionally, the first microdermabrasion treatment is bilateral filtering treatment, guided filtering treatment or weighted least squares treatment;

The second microdermabrasion processing is bilateral filtering processing, guided filtering processing or weighted least square processing.

Optionally, as shown in Figure 14, the device 50 also includes:

The collection module 505 is configured to collect images through the camera assembly;

The recognition module 506 is configured to perform face recognition on the collected images;

The determining module 507 is configured to determine an image in a predetermined area including a human face image in the collected image as an initial human face image when there is a human face image in the collected image.

In the face image processing device provided by the embodiments of the present disclosure, the image acquisition module in the device collects images through the camera component, the face recognition module can perform face recognition on the collected images, and the determination module will include human faces in the collected images. The image in the predetermined area of the face image is determined to be the initial face image, and the first microdermabrasion module can perform the first microdermabrasion process on the initial human face image to obtain the basic image, and the acquisition module acquires the basic image based on the initial human face image. For the detail image of the texture feature of the skin in the initial face image, the second microdermabrasion module can perform a second microdermabrasion process on the detail image to obtain the target detail image, and the superposition module can combine the target detail image with the target detail image The above-mentioned basic image is superimposed to obtain the target face image, and the skin in the obtained target face image has less impurities, effectively retains the texture characteristics of the skin, and increases the texture of the skin.

An embodiment of the present disclosure provides a face image processing device 60, as shown in FIG. 15, the device includes:

processing component 601;

memory 602 for storing executable instructions of said processing components;

Wherein, the processing component is configured to execute any one of the face image processing methods provided by the embodiments of the present disclosure.

Fig. 16 is a block diagram of an apparatus 700 for processing a face image according to an exemplary embodiment. For example, the apparatus 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like.

16, the device 700 may include one or more of the following components: a processing component 7002, a memory 7004, a power supply component 7006, a multimedia component 7008, an audio component 7010, an input/output (I/O) interface 7012, a sensor component 7014, and communication component 7016.

The processing component 7002 generally controls the overall operations of the device 700, such as those associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 7002 may include one or more processors 7020 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 7002 may include one or more modules that facilitate interaction between processing component 7002 and other components. For example, processing component 7002 may include a multimedia module to facilitate interaction between multimedia component 7008 and processing component 7002.

The memory 7004 is configured to store various types of data to support operations at the device 700 . Examples of such data include instructions for any application or method operating on device 700, contact data, phonebook data, messages, pictures, videos, and the like. The memory 7004 can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.

Power supply component 7006 provides power to various components of device 700 . Power components 7006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for device 700 .

The multimedia component 7008 includes a screen that provides an output interface between the device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense a boundary of a touch or swipe action, but also detect duration and pressure associated with the touch or swipe action. In some embodiments, the multimedia component 7008 includes a front camera and/or a rear camera. When the device 700 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capability.

The audio component 7010 is configured to output and/or input audio signals. For example, the audio component 7010 includes a microphone (MIC), which is configured to receive external audio signals when the device 700 is in operation modes, such as call mode, recording mode and voice recognition mode. Received audio signals may be further stored in memory 7004 or sent via communication component 7016 . In some embodiments, the audio component 7010 also includes a speaker for outputting audio signals.

The I/O interface 7012 provides an interface between the processing component 7002 and a peripheral interface module, and the above peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: a home button, volume buttons, start button, and lock button.

Sensor assembly 7014 includes one or more sensors for providing status assessments of various aspects of device 700 . For example, the sensor component 7014 can detect the open/closed state of the device 700, the relative positioning of components, such as the display and keypad of the device 700, and the sensor component 7014 can also detect a change in the position of the device 700 or a component of the device 700 , the presence or absence of user contact with the device 700 , the device 700 orientation or acceleration/deceleration and the temperature change of the device 700 . Sensor assembly 7014 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 7014 may also include light sensors, such as CMOS or CCD image sensors, for use in imaging applications. In some embodiments, the sensor component 7014 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.

The communication component 7016 is configured to facilitate wired or wireless communication between the apparatus 700 and other devices. The device 700 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 7016 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 7016 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, apparatus 700 may be programmed by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation for performing the methods described above.

In an exemplary embodiment, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory 7004 including instructions, which can be executed by the processor 7020 of the device 700 to implement the above method. For example, the non-transitory computer readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

A non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the device 700, the device 700 can execute the method for processing a face image provided in the above-mentioned embodiments.

Regarding the apparatus in the foregoing embodiments, the specific manner in which each module executes operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

Other embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure . The specification and examples are to be considered exemplary only, with the true scope and spirit of the disclosure indicated by the appended claims.

It should be understood that the present disclosure is not limited to the precise constructions which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (10)

1. A processing method of a human face image, applied to a terminal, is characterized in that, comprising: Carrying out the first microdermabrasion treatment on the initial face image to obtain the basic image; Making a difference between the initial human face image and the basic image to obtain a detail image, the detailed image is used to reflect the texture characteristics of the skin in the initial human face image; performing nonlinear transformation on the detail image to obtain a transformed detail image; performing a second skin smoothing process on the transformed detail image to obtain a target detail image; The target detail image is superimposed on the basic image to obtain a target face image. 2. The method of claim 1, wherein, The non-linear transformation of the detail image to obtain the transformed detail image includes: Based on a polynomial transformation formula, the detail image is nonlinearly transformed to obtain a transformed detail image, and the polynomial transformation formula is:

Wherein, D1 is the i-th pixel value of the transformed detail image, the D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n , n is the total number of pixel values in the detail image. 3. The method of claim 1, wherein, Both the first microdermabrasion treatment and the second microdermabrasion treatment are edge-preserving filtering treatments; Wherein, the first microdermabrasion treatment is bilateral filtering treatment, guided filtering treatment or weighted least squares treatment; The second microdermabrasion processing is bilateral filtering processing, guided filtering processing or weighted least square processing. 4. The method according to claim 1, wherein the method further comprises: Capture images through camera components; Perform face recognition on the collected images; When there is a human face image in the collected image, an image in a predetermined area including the human face image in the collected image is determined as an initial human face image. 5. A processing device for face images, applied to a terminal, characterized in that it comprises: The first microdermabrasion module is configured to perform a first microdermabrasion process on the initial face image to obtain a basic image; An acquisition module configured to acquire a detail image based on the initial face image, and the detail image is used to reflect texture features of the skin in the initial face image; The second microdermabrasion module is configured to perform a second microdermabrasion process on the detail image to obtain a target detail image; an overlay module configured to overlay the target detail image and the base image to obtain a target face image; The acquisition module is configured as: Making a difference between the initial face image and the basic image to obtain the detail image; The second microdermabrasion module includes: a transformation sub-module configured to perform nonlinear transformation on the detail image to obtain a transformed detail image; The skin smoothing submodule is configured to perform the second skin smoothing process on the transformed detail image to obtain the target detail image. 6. The device according to claim 5, characterized in that, The transformation submodule is configured as: Based on a polynomial transformation formula, the detail image is nonlinearly transformed to obtain a transformed detail image, and the polynomial transformation formula is:

Wherein, D1 is the i-th pixel value of the transformed detail image, the D is the i-th pixel value of the detail image, a and b are preset non-zero transformation coefficients, 1≤i≤n , n is the total number of pixel values in the detail image. 7. The device of claim 5, wherein: Both the first microdermabrasion treatment and the second microdermabrasion treatment are edge-preserving filtering treatments; Wherein, the first microdermabrasion treatment is bilateral filtering treatment, guided filtering treatment or weighted least squares treatment; The second microdermabrasion processing is bilateral filtering processing, guided filtering processing or weighted least square processing. 8. The device according to claim 5, further comprising: The acquisition module is configured to acquire images through the camera component; A recognition module configured to perform face recognition on the collected images; The determining module is configured to determine an image in a predetermined area including a human face image in the collected image as an initial human face image when there is a human face image in the collected image. 9. A processing device for human face images, characterized in that the device comprises: Processing components; memory for storing executable instructions of said processing component; Wherein, the processing component is configured to execute the method for processing a face image according to any one of claims 1 to 4. 10. A computer-readable storage medium, characterized in that instructions are stored in the computer-readable storage medium, and when the readable storage medium is run on a processing component, the processing component is made to perform the process according to claims 1 to 4. Any one of the processing methods of human face images.

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