CN114529474A - 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. An image processing method includes: acquiring an original image; establishing an image pyramid corresponding to the original image; determining a low-frequency map of the image pyramid as an image to be processed; performing a blurring process on the image to be processed to obtain a first image; An image is generated, and a second image is generated; according to the histogram distribution of the image to be processed, a corresponding first curve is determined, wherein the first curve is used to increase or decrease the brightness value of the first target area in the second image; based on the first curve Process the second image; if the processed second image satisfies the preset condition, output the processed second 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, algorithmic skin resurfacing is the basic effect in beauty. The existing traditional skin resurfacing algorithms generally use frequency division for filtering. The uniformity of the brightness of the flaws results in a poor microdermabrasion effect.

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 skin grinding effect.

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

get the original image;

Build an image pyramid corresponding to the original image;

Determine the low-frequency map of the image pyramid as the image to be processed;

Blur the image to be processed to obtain a first image;

generating a second image according to the to-be-processed image and the first image;

Determine a corresponding first curve according to the histogram distribution of the image to be processed, wherein the first curve is used to increase or decrease the brightness value of the first target area in the second image;

processing the second image based on the first curve;

In the case that the processed second image satisfies the preset condition, the processed second image is output.

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

The acquisition module is used to acquire the original image;

A building module is used to build an image pyramid corresponding to the original image;

A determination module, used to determine the low-frequency map of the image pyramid as the image to be processed;

a processing module, used for blurring the to-be-processed image to obtain a first image;

a generating module for generating a second image according to the to-be-processed image and the first image;

The determining module is further configured to determine the corresponding first curve according to the histogram distribution of the image to be processed, wherein the first curve is used to increase or decrease the brightness value of the first target area in the second image;

a processing module, further configured to process the second image based on the first curve;

The output module is configured to output the processed second image under the condition that the processed second image satisfies the preset condition.

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.

The embodiment of the present application determines the image to be processed based on the image pyramid of the original image, and further performs Blur (blur) processing on the image to be processed, and determines the mask area to be brightened based on the pictures before and after processing, and Based on the uniformity of the histogram distribution of the original image to be processed, determine the first area for brightening the mask area, and based on the first curve, perform brightening processing on the mask area to be brightened, so as to effectively brighten The dark areas in the face image, such as spots, scars and other areas, will not be brightened by the enhancement of the face, so the three-dimensional structure of the face will not be damaged, and there is no need to use the AI algorithm, which will occupy and reduce the performance of the device. The overhead is relatively small, which realizes the uniformity of the brightness of the flawed part while maintaining the three-dimensional structure of the face, and improves the skin resurfacing effect.

Description of drawings

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

FIG. 2 shows a schematic diagram of an algorithm of an image processing method according to an embodiment of the present application;

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

FIG. 4 shows a structural block diagram of an electronic device according to an embodiment of the present application;

FIG. 5 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 a flowchart of an image processing method according to an embodiment of the present application. As shown in FIG. 1 , the method includes:

Step 102, obtaining the original image;

Step 104, establishing an image pyramid corresponding to the original image;

Step 106, determining the low-frequency map of the image pyramid as the image to be processed;

Step 108, blurring the image to be processed to obtain a first image;

Step 110, generating a second image according to the image to be processed and the first image;

Step 112, according to the histogram distribution of the image to be processed, determine the corresponding first curve;

In step 112, the first curve is used to increase or decrease the brightness value of the first target area in the second image;

Step 114, processing the second image based on the first curve;

Step 116 , if the processed second image satisfies the preset condition, output the processed second image.

In the embodiment of the present application, the original image, that is, the input algorithm image, is specifically an image selected by the user and needs to be processed for beauty. Since the resolution and size of the original image are large, after the original image is received, first, an image pyramid corresponding to the original image is established.

Specifically, the image pyramid is a collection of images whose resolution is gradually reduced. The higher the level of the image, the smaller the image size and the lower the resolution. Reducing the size and resolution of the original image according to the image pyramid can effectively reduce the performance overhead of the microdermabrasion algorithm and improve the microdermabrasion efficiency.

Specifically, the fourth layer image from the top of the image pyramid can be selected as the image to be processed, which is beneficial to reduce the performance overhead of the microdermabrasion algorithm while preserving the details.

The image to be processed is specifically a face image. After the image to be processed is received, first, the image to be processed is blurred. Specifically, the image to be processed can be blurred by performing Blur processing. The processing area of the Blur processing may be a quarter of the area of the face part in the image to be processed. After performing Blur processing, a first blurred image corresponding to the image to be processed is obtained. According to the first image and the to-be-processed image before the blurring process, a second image is obtained, and the second image is the mask area to be brightened.

Further, based on the distribution of the histogram of the image to be processed, specifically based on the uniformity of the distribution of the histogram of the image to be processed, combined with a preset empirical mapping relationship, a corresponding first curve is determined, and the first curve is specifically used to adjust the first curve. The first target area in the two images, that is, the brightness curve of the mask area to be brightened.

Through the first curve, the brightness value of the second image can be brightened or darkened. Taking the brightening process first as an example, through the first curve, the second image, that is, the mask area in the image to be processed, is subjected to LUT (Look-Up-Table) mapping operation, so as to brighten the dark parts of the face. area (dirty area, such as spots, scars, etc.), after processing, determine whether the processed second image satisfies the preset conditions, if so, output the second image as the mask area after dermabrasion, and finally get A photo of a face after beautification.

In this embodiment of the present application, by performing Blur processing on the image to be processed, based on the pictures before and after processing, the mask area to be brightened is determined, and based on the uniformity of the distribution of the histogram of the original image to be processed, the mask area used for brightening processing is determined Based on the first curve, the mask area to be brightened is brightened, so that the dark areas in the face image, such as spots and scars, can be effectively brightened. It does not damage the three-dimensional structure of the face, and does not need to use AI algorithms, so it occupies less equipment performance and costs less, and realizes the brightness of the flawed part while maintaining the three-dimensional structure of the face. Homogenizes and improves the dermabrasion effect.

In some embodiments of the present application, the image to be processed includes N first pixels, the first image includes N second pixels, the N first pixels are in one-to-one correspondence with the N second pixels, and N is a positive integer;

Generate a second image according to the image to be processed and the first image, including:

determining a first brightness value set according to the image to be processed, where the first brightness value set includes the brightness values of N first pixels;

determining, according to the first image, a second set of luminance values, where the second set of luminance values includes luminance values of N second pixels;

A second image is generated from the first set of luminance values and the second set of luminance values.

In this embodiment of the present application, when determining the second image, that is, the mask area to be brightened, the mask area to be brightened may be determined according to the brightness difference between the original to-be-processed image and the first image.

Specifically, the first image is obtained by Blur processing the image to be processed, so the pixels in the first image and the second image are in one-to-one correspondence. The pixels in the image to be processed are denoted as first pixels, and the pixels in the first image are denoted as second pixels.

Because Blur processing can produce high contrast in the image, it can make bright pixels brighter and dark pixels darker in the image. Among them, the brightness value of the image refers to the set of brightness values of the pixels in the image. Therefore, the brightness value of the original image to be processed is the set of N first brightness values corresponding to the N first pixels. Similarly, , the luminance value of the first image, that is, the set of N second luminance values corresponding to the N second pixels.

Through the first set of luminance values and the second set of luminance values, the dark area in the image to be processed, that is, the mask area that needs to be brightened, that is, the above-mentioned second image can be obtained.

In this application, the mask area to be brightened, that is, the dirty part in the face image, is determined according to the image brightness before and after Blur processing, and targeted brightening processing is performed on this part, which can ensure the overall three-dimensional structure of the face image. Under the circumstance, the brightness of the underlying flaws is brightened and homogenized, which effectively improves the microdermabrasion effect.

In some embodiments of the present application, generating the second image according to the first set of brightness values and the second set of brightness values includes:

determining the first difference set according to the difference between the first luminance value set and the second luminance value set;

According to the M difference values whose luminance values are less than zero in the first difference value set, the corresponding M first target pixels are determined, where M is a positive integer less than N;

The M first target pixels are removed from the to-be-processed image to obtain a second image.

In this embodiment of the present application, the first set of luminance values is a set of luminance values of N first pixels in the image to be processed. Similarly, the second set of luminance values is a set of luminance values of N second pixels in the first image. Among them, the brightness value of the pixel point is the pixel value, and its range is [0, 255].

When calculating the first brightness value set and the second brightness value set, the brightness value of the corresponding first pixel is subtracted from the brightness value of the corresponding second pixel to obtain N difference values. The set of N difference values is The above-mentioned first set of difference values.

Since the first image is an image obtained by Blur processing on the basis of the original image to be processed, among the N second pixels, the bright pixels become brighter, that is, the brightness value increases, and the dark pixels become brighter. Change the case, that is, the brightness value is reduced. Thus, in the first image, the luminance values of "bright" pixels become higher, and the luminance values of "dark" pixels become lower.

Therefore, when calculating the difference between the first set of brightness values and the second set of brightness values, since the brightness values of the "bright" pixels in the first image become larger, they will be larger than the corresponding pixels in the original image to be processed. Brightness value, that is, for "bright" pixels, the difference will be less than zero.

Similarly, in the second image, the brightness value of the "dark" pixels is smaller, so it will be smaller than the brightness value of the corresponding pixels in the original image to be processed, that is to say, for the "dark" pixels, the difference will be Greater than zero.

Therefore, after obtaining the first set of difference values between the first set of luminance values and the second set of luminance values, M difference values with luminance values less than zero are determined in it, which is equivalent to obtaining M "" Bright" pixels, these M bright pixels do not need to be brightened, the pixels of normal faces, and the remaining N-M pixels are the "dirty" parts of the face image that need to be brightened.

In the image to be processed, after removing M bright pixels, only N-M dark pixels that need to be brightened are obtained, that is, a second image is obtained. Brightening the dark pixels in the second image based on the first curve is equivalent to "deepening" processing, which can effectively remove the dirty part in the face image without adjusting the normal pixels, ensuring that The three-dimensional structure of the human face improves the effect of microdermabrasion.

In some embodiments of the present application, after processing the second image based on the first curve, the image processing method further includes:

In the case where the processed second image does not meet the preset condition, a corresponding second curve is determined according to the histogram distribution, wherein the second curve is used to increase or decrease the brightness value of the first target area, and the preset condition includes The intensity value of the processed second image is less than a preset threshold;

blurring the processed second image to obtain a third image;

According to the third image, determine a third set of brightness values;

determining a second difference set according to the difference between the second set of brightness values and the third set of brightness values;

According to the second difference set, O difference values whose brightness values are greater than zero are determined to correspond to O second target pixels, where O is a positive integer less than N;

In the processed second image, remove 0 second target pixels to obtain a fourth image;

processing the fourth image based on the second image;

The processed fourth image is updated to the image to be processed, and the step of blurring the image to be processed is performed again.

In the embodiment of the present application, the preset condition refers to that the intensity value of the second image is smaller than the preset threshold. The intensity value of the second image refers to a set of pixel values of each pixel in the second image after being brightened by the first curve. If after the first curve is highlighted, the pixel value of each pixel in the second image is smaller than the preset pixel value threshold, that is, the above-mentioned preset threshold.

It can be understood that the value range of the pixel value is 0 to 255, and the value range of the preset threshold is also between 0 and 255, which can be freely set according to actual needs.

If the second image does not meet the preset conditions after the first curve is highlighted, the corresponding second curve is determined according to the uniformity of the distribution of the histogram of the image to be processed and the preset empirical mapping relationship. The second curve is specifically used to reduce the brightness of the mask area to be brightened.

After the second curve is obtained, the second image is further subjected to blurring processing, namely Blur processing, to obtain a processed third image, wherein the processing area of the Blur processing can be the area of the face part in the image to be processed. quarter. After performing the Blur processing, a blurred third image corresponding to the image to be processed is obtained.

Further, in the same way as determining the brightness values of the image to be processed and the first image, a third brightness value set of the second image and a fourth brightness value set of the third image are determined. Wherein, compared with the second image, in the third image, the brightness value of the "bright" pixels becomes higher, and the brightness value of the "dark" pixels becomes lower.

Therefore, after calculating the difference between the third set of luminance values and the fourth set of luminance values, and after obtaining the second set of difference values, determine O difference values with luminance values greater than zero in it, which is equivalent to obtaining M in the image to be processed. This dark and bright pixel needs to be brightened. In the face image, the "dirty" part that needs to be brightened.

In the second image, remove the second target pixels corresponding to the 0 differences, that is, obtain the mask area that needs to be darkened, that is, the fourth image, and perform the LUT mapping operation processing again on the fourth image through the second curve , so as to darken the mask area, which is equivalent to "dodge" processing.

The fourth image processed by "lightening" is regarded as a new image to be processed, and step 102 in FIG. 1 is re-executed until the second image obtained after final processing satisfies the preset condition, and it is determined that the abrasion is over.

The embodiments of the present application can effectively brighten the dark areas in the face image, such as spots, scars and other areas, the processing will not enhance the face enhancement and brighten, so the three-dimensional structure of the face will not be damaged, and AI does not need to be used The algorithm, which occupies less equipment performance and costs less, realizes the uniformity of the brightness of the flawed part while maintaining the three-dimensional structure of the face, and improves the effect of skin resurfacing.

In some embodiments of the present application, before processing the second image based on the first curve, or before processing the second image based on the second curve, the method further includes:

obtaining a preset mask, where the preset mask is used to keep the brightness value of the second target area of the second image unchanged;

The second target area is processed by a preset mask.

In the embodiment of the present application, when the second image is brightened based on the first curve, or the second image is darkened based on the second image, a preset mask is obtained, and the preset mask is a facial features protection mask, It is used to protect the facial features of the human face, so as to avoid misjudging the facial features of the human face as dirty and brighten them, and to preserve the facial features of the human face.

Specifically, after processing the second target area of the second image, that is, the facial features area, through the preset mask, the brightness values in the facial features area will not receive the first curve and the second curve under the protection of the preset mask. Therefore, the facial features of the face will not be affected by the microdermabrasion algorithm, and the details of the facial features can be accurately restored to ensure the microdermabrasion effect.

In some embodiments of the present application, after outputting the processed second image, the method further includes: processing the original image through the second image to obtain a processed target image.

In the embodiment of the present application, after outputting the second image as the mask area after dermabrasion, the mask area is processed with the original image, that is, the original face image, so as to brighten the dark area in the face image , such as spots, scars and other areas, and get the face image after microdermabrasion. This process will not brighten the face enhancement, so it will not damage the three-dimensional structure of the face, and does not need to use AI algorithms, the occupation and cost of equipment performance are small, and the three-dimensional structure of the face is maintained. It achieves the uniformity of the brightness of the flawed part and improves the effect of microdermabrasion. FIG. 2 shows a schematic diagram of an algorithm of an image processing method according to an embodiment of the present application. As shown in FIG. 2 , the algorithm includes:

After the original image is obtained, the image to be processed is obtained through the image pyramid, and the first curve and the second curve are obtained according to the histogram experience.

Perform blur processing on the image to be processed to obtain a first image, and obtain a second image with high contrast according to the image to be processed and the first image after blur processing, and protect the target area in the second image through a preset mask, and process it based on the first curve to obtain a brightened second image.

Determine whether the second image satisfies the preset condition, if so, output the second image, if not, perform blur processing on the second image to obtain a third image with high contrast, and according to the second image and the third image, A high-contrast fourth image is obtained. The target area in the fourth image is protected by a preset mask, and processed based on the second curve to obtain a darkened fourth image.

The darkened fourth image is used as a new image to be processed, and algorithm processing steps such as blur processing are performed again until the generated second image satisfies the preset conditions.

In some embodiments of the present application, an image processing apparatus is provided. FIG. 3 shows a structural block diagram of an image processing apparatus according to an embodiment of the present application. As shown in FIG. 3 , the image processing apparatus 300 includes:

an acquisition module 302, configured to acquire an original image;

Establishing module 304, for establishing the image pyramid corresponding to the original image;

A determination module 306, configured to determine the low-frequency map of the image pyramid as the image to be processed;

a processing module 308, configured to perform blurring processing on the image to be processed to obtain a first image;

a generating module 310, configured to generate a second image according to the to-be-processed image and the first image;

A determination module 306, configured to determine a corresponding first curve according to the histogram distribution of the image to be processed, wherein the first curve is used to increase or decrease the brightness value of the target area in the second image;

a processing module 308, further configured to process the second image based on the first curve;

The output module 312 is configured to output the processed second image under the condition that the processed second image satisfies the preset condition.

In the embodiment of the present application, the original image, that is, the input algorithm image, is specifically an image selected by the user and needs to be processed for beauty. Since the resolution and size of the original image are large, after the original image is received, first, an image pyramid corresponding to the original image is established.

Specifically, the image pyramid is a collection of images whose resolution is gradually reduced. The higher the level of the image, the smaller the image size and the lower the resolution. Reducing the size and resolution of the original image according to the image pyramid can effectively reduce the performance overhead of the microdermabrasion algorithm and improve the microdermabrasion efficiency.

Specifically, the fourth layer image from the top of the image pyramid can be selected as the image to be processed, which is beneficial to reduce the performance overhead of the microdermabrasion algorithm while preserving the details.

The image to be processed is specifically a face image. After the image to be processed is received, first, the image to be processed is blurred. Specifically, the image to be processed can be blurred by performing Blur processing. The processing area of the Blur processing may be a quarter of the area of the face part in the image to be processed. After performing Blur processing, a first blurred image corresponding to the image to be processed is obtained. According to the first image and the to-be-processed image before the blurring process, a second image is obtained, and the second image is the mask area to be brightened.

Further, based on the histogram distribution of the to-be-processed image, specifically based on the uniformity of the histogram distribution of the to-be-processed image, combined with a preset empirical mapping relationship, a corresponding first curve is determined, and the first curve is specifically used to adjust the first curve. The first target area in the two images, that is, the brightness curve of the mask area to be brightened.

Through the first curve, the brightness value of the second image can be brightened or darkened. Taking the brightening process first as an example, through the first curve, perform LUT (Look-Up-Table) mapping operation processing on the second image, that is, the mask area in the image to be processed, so as to brighten the dark parts of the face. area (dirty area, such as spots, scars, etc.), after processing, determine whether the processed second image satisfies the preset conditions, if so, output the second image as the mask area after dermabrasion, and finally get A photo of a face after beautification.

FIG. 2 shows a schematic diagram of an algorithm of an image processing method according to an embodiment of the present application. As shown in FIG. 2 , the algorithm includes:

After the original image is obtained, the image to be processed is obtained through the image pyramid, and the first curve and the second curve are obtained according to the histogram experience.

Perform blur processing on the image to be processed to obtain a first image, and obtain a second image with high contrast according to the image to be processed and the first image after blur processing, and protect the target area in the second image through a preset mask, and process it based on the first curve to obtain a brightened second image.

Determine whether the second image satisfies the preset condition, if so, output the second image, if not, perform blur processing on the second image to obtain a third image with high contrast, and according to the second image and the third image, A high-contrast fourth image is obtained. The target area in the fourth image is protected by a preset mask, and processed based on the second curve to obtain a darkened fourth image.

The darkened fourth image is used as a new image to be processed, and algorithm processing steps such as blur processing are performed again until the generated second image satisfies the preset conditions.

In this embodiment of the present application, by performing Blur processing on the image to be processed, based on the pictures before and after processing, the mask area to be brightened is determined, and based on the uniformity of the distribution of the histogram of the original image to be processed, the mask area used for brightening processing is determined Based on the first curve, the mask area to be brightened is brightened, so that the dark areas in the face image, such as spots and scars, can be effectively brightened. It does not damage the three-dimensional structure of the face, and does not need to use AI algorithms, so it occupies less equipment performance and costs less, and realizes the brightness of the flawed part while maintaining the three-dimensional structure of the face. Homogenizes and improves the dermabrasion effect.

In some embodiments of the present application, the image to be processed includes N first pixels, the first image includes N second pixels, the N first pixels are in one-to-one correspondence with the N second pixels, and N is a positive integer;

The determining module is further configured to determine, according to the image to be processed, a first set of brightness values, where the first set of brightness values includes the brightness values of N first pixels; according to the first image, determine a second set of brightness values, the second set of brightness values Including the brightness values of the N second pixels;

The generating module is further configured to generate a second image according to the first luminance value set and the second luminance value set.

In this embodiment of the present application, when determining the second image, that is, the mask area to be brightened, the mask area to be brightened may be determined according to the brightness difference between the original to-be-processed image and the first image.

Specifically, the first image is obtained by Blur processing the image to be processed, so the pixels in the first image and the second image are in one-to-one correspondence. The pixels in the image to be processed are denoted as first pixels, and the pixels in the first image are denoted as second pixels.

Because Blur processing can produce high contrast in the image, it can make bright pixels brighter and dark pixels darker in the image. Among them, the brightness value of the image refers to the set of brightness values of the pixels in the image. Therefore, the brightness value of the original image to be processed is the set of N first brightness values corresponding to the N first pixels. Similarly, , the luminance value of the first image, that is, the set of N second luminance values corresponding to the N second pixels.

Through the first set of luminance values and the second set of luminance values, the dark area in the image to be processed, that is, the mask area that needs to be brightened, that is, the above-mentioned second image can be obtained.

In this application, the mask area to be brightened, that is, the dirty part in the face image, is determined according to the image brightness before and after Blur processing, and targeted brightening processing is performed on this part, which can ensure the overall three-dimensional structure of the face image. Under the circumstance, the brightness of the underlying flaws is brightened and homogenized, which effectively improves the microdermabrasion effect.

In some embodiments of the present application, the determining module is further configured to determine the first difference value set according to the difference between the first brightness value set and the second brightness value set; according to the first difference value set, the brightness value is less than zero The M difference values of , determine the corresponding M first target pixels, where M is a positive integer smaller than N; the processing module is also used to remove the M first target pixels in the to-be-processed image to obtain a second image.

In this embodiment of the present application, the first set of luminance values is a set of luminance values of N first pixels in the image to be processed. Similarly, the second set of luminance values is a set of luminance values of N second pixels in the first image. Among them, the brightness value of the pixel point is the pixel value, and its range is [0, 255].

When calculating the first brightness value set and the second brightness value set, the brightness value of the corresponding first pixel is subtracted from the brightness value of the corresponding second pixel to obtain N difference values. The set of N difference values is The above-mentioned first set of difference values.

Since the first image is an image obtained by Blur processing on the basis of the original image to be processed, among the N second pixels, the bright pixels become brighter, that is, the brightness value increases, and the dark pixels become brighter. Change the case, that is, the brightness value is reduced. Thus, in the first image, the luminance values of "bright" pixels become higher, and the luminance values of "dark" pixels become lower.

Therefore, when calculating the difference between the first set of brightness values and the second set of brightness values, since the brightness values of the "bright" pixels in the first image become larger, they will be larger than the corresponding pixels in the original image to be processed. Brightness value, that is, for "bright" pixels, the difference will be less than zero.

Similarly, in the second image, the brightness value of the "dark" pixels is smaller, so it will be smaller than the brightness value of the corresponding pixels in the original image to be processed, that is to say, for the "dark" pixels, the difference will be Greater than zero.

Therefore, after obtaining the first set of difference values between the first set of luminance values and the second set of luminance values, M difference values with luminance values less than zero are determined in it, which is equivalent to obtaining M "" Bright" pixels, these M bright pixels do not need to be brightened, the pixels of normal faces, and the remaining N-M pixels are the "dirty" parts of the face image that need to be brightened.

In the image to be processed, after removing M bright pixels, only N-M dark pixels that need to be brightened are obtained, that is, a second image is obtained. Brightening the dark pixels in the second image based on the first curve is equivalent to "deepening" processing, which can effectively remove the dirty part in the face image without adjusting the normal pixels, ensuring that The three-dimensional structure of the human face improves the effect of microdermabrasion.

In some embodiments of the present application, the determining module is further configured to determine a corresponding second curve according to the histogram distribution when the processed second image does not meet the preset condition, wherein the second curve is used for Increase or decrease the brightness value of the first target area, the preset condition includes that the intensity value of the processed second image is less than a preset threshold;

The processing module is further used for blurring the processed second image to obtain a third image;

The determining module is further configured to determine a third brightness value set according to the second image; determine a fourth brightness value set according to the third image; determine a second difference according to the difference between the third brightness value set and the fourth brightness value set value set; according to the second difference value set, O difference values whose brightness value is greater than zero, determine the corresponding O second target pixels, and O is a positive integer less than N;

The processing module is further configured to remove 0 second target pixels in the processed second image to obtain a fourth image; process the fourth image based on the second image; update the processed fourth image to an image to be processed, And execute the step of blurring the image to be processed again.

In the embodiment of the present application, the preset condition refers to that the intensity value of the second image is smaller than the preset threshold. The intensity value of the second image refers to a set of pixel values of each pixel in the second image after being brightened by the first curve. If after the first curve is highlighted, the pixel value of each pixel in the second image is smaller than the preset pixel value threshold, that is, the above-mentioned preset threshold.

It can be understood that the value range of the pixel value is 0 to 255, and the value range of the preset threshold is also between 0 and 255, which can be freely set according to actual needs.

If the second image does not meet the preset conditions after the first curve is highlighted, the corresponding second curve is determined according to the uniformity of the distribution of the histogram of the image to be processed and the preset empirical mapping relationship. The second curve is specifically used to reduce the brightness of the mask area to be brightened.

After the second curve is obtained, the second image is further subjected to blurring processing, namely Blur processing, to obtain a processed third image, wherein the processing area of the Blur processing can be the area of the face part in the image to be processed. quarter. After performing the Blur processing, a blurred third image corresponding to the image to be processed is obtained.

Further, in the same way as determining the brightness values of the image to be processed and the first image, a third brightness value set of the second image and a fourth brightness value set of the third image are determined. Wherein, compared with the second image, in the third image, the brightness value of the "bright" pixels becomes higher, and the brightness value of the "dark" pixels becomes lower.

Therefore, after calculating the difference between the third set of luminance values and the fourth set of luminance values, and after obtaining the second set of difference values, determine O difference values with luminance values greater than zero in it, which is equivalent to obtaining M in the image to be processed. This dark and bright pixel needs to be brightened. In the face image, the "dirty" part that needs to be brightened.

In the second image, remove the second target pixels corresponding to the 0 differences, that is, obtain the mask area that needs to be darkened, that is, the fourth image, and perform the LUT mapping operation processing again on the fourth image through the second curve , so as to darken the mask area, which is equivalent to "dodge" processing.

The fourth image processed by "lightening" is regarded as a new to-be-processed image, until the second image obtained after final processing satisfies the preset condition, it is determined that the abrasion is over.

The embodiments of the present application can effectively brighten the dark areas in the face image, such as spots, scars and other areas, the processing will not enhance the face enhancement and brighten, so the three-dimensional structure of the face will not be damaged, and AI does not need to be used The algorithm, which occupies less equipment performance and costs less, realizes the uniformity of the brightness of the flawed part while maintaining the three-dimensional structure of the face, and improves the effect of skin resurfacing.

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

an acquisition module, further configured to acquire a preset mask, where the preset mask is used to keep the brightness value of the second target area of the second image unchanged;

The processing module is further configured to process the second target area through a preset mask.

In the embodiment of the present application, when the second image is brightened based on the first curve, or the second image is darkened based on the second image, a preset mask is obtained, and the preset mask is a facial features protection mask, It is used to protect the facial features of the human face, so as to avoid misjudging the facial features of the human face as dirty and brighten them, and to preserve the facial features of the human face.

Specifically, after the target area of the second image, that is, the facial features area, is processed through the preset mask, the brightness values in the facial features area will not receive the first curve and the second curve under the protection of the preset mask. Therefore, the facial features of the face will not be affected by the microdermabrasion algorithm, and the details of the facial features can be accurately restored to ensure the microdermabrasion effect.

In some embodiments of the present application, the processing module is further configured to process the original image by using the second image to obtain the processed target image. In the embodiment of the present application, after outputting the second image as the mask area after dermabrasion, the mask area is processed with the original image, that is, the original face image, so as to brighten the dark area in the face image , such as spots, scars and other areas, and get the face image after microdermabrasion. This process will not brighten the face enhancement, so it will not damage the three-dimensional structure of the face, and does not need to use AI algorithms, the occupation and cost of equipment performance are small, and the three-dimensional structure of the face is maintained. It achieves the uniformity of the brightness of the flawed part and improves the effect of microdermabrasion.

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 PDA, a vehicle 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 (NASs) ), personal computer (personal computer, PC), television (television, TV), teller machine or 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 in the foregoing method embodiment, and to avoid repetition, details are not described herein again.

Optionally, an embodiment of the present application further provides an electronic device. FIG. 4 shows a structural block diagram of an electronic device according to an embodiment of the present application. As shown in FIG. 4 , the electronic device 400 includes a processor 402, a memory 404, a storage A program or instruction that is on the memory 404 and can be run on the processor 402. When the program or instruction is executed by the processor 402, each process of the above method embodiment can be achieved, and the same technical effect can be achieved. 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. 5 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.

The electronic device 500 includes but is not limited to: a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509, and a processor 510, etc. part.

Those skilled in the art can understand that the electronic device 500 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 510 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. 5 does not constitute a limitation on 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 510 is used to obtain the original image;

Establish an image pyramid corresponding to the original image; determine the low-frequency map of the image pyramid as the image to be processed;

Blur the image to be processed to obtain a first image;

generating a second image according to the to-be-processed image and the first image;

Determine a corresponding first curve according to the histogram distribution of the image to be processed, wherein the first curve is used to increase or decrease the brightness value of the first target area in the second image;

processing the second image based on the first curve;

In the case that the processed second image satisfies the preset condition, the processed second image is output.

In this embodiment of the present application, by performing Blur processing on the image to be processed, based on the pictures before and after processing, the mask area to be brightened is determined, and based on the uniformity of the distribution of the histogram of the original image to be processed, the mask area used for brightening processing is determined Based on the first curve, the mask area to be brightened is brightened, so that the dark areas in the face image, such as spots and scars, can be effectively brightened. It does not damage the three-dimensional structure of the face, and does not need to use AI algorithms, so it occupies less equipment performance and costs less, and realizes the brightness of the flawed part while maintaining the three-dimensional structure of the face. Homogenizes and improves the dermabrasion effect.

Optionally, the image to be processed includes N first pixels, the first image includes N second pixels, the N first pixels are in one-to-one correspondence with the N second pixels, and N is a positive integer;

The processor 510 is further configured to determine a first brightness value set according to the image to be processed, where the first brightness value set includes the brightness values of N first pixels;

determining, according to the first image, a second set of luminance values, where the second set of luminance values includes luminance values of N second pixels;

A second image is generated from the first set of luminance values and the second set of luminance values.

According to the image brightness before and after Blur processing, the application determines the mask area to be brightened, that is, the dirty part in the face image, and performs targeted brightening processing on this part, which can ensure the overall three-dimensional structure of the face image. It brightens and homogenizes the brightness of the underlying flaws, effectively improving the effect of microdermabrasion.

Optionally, the processor 510 is further configured to determine the first difference value set according to the difference between the first brightness value set and the second brightness value set;

According to the M difference values whose luminance values are less than zero in the first difference value set, the corresponding M first target pixels are determined, where M is a positive integer less than N;

The M first target pixels are removed from the to-be-processed image to obtain a second image.

The embodiments of the present application can effectively remove the dirty part in the face image, and at the same time, the normal pixels will not be adjusted, thus ensuring the three-dimensional structure of the face and improving the effect of microdermabrasion.

Optionally, the processor 510 is further configured to determine a corresponding second curve according to the histogram distribution under the condition that the processed second image does not meet the preset condition, wherein the second curve is used to increase or decrease the first curve. a brightness value of a target area, the intensity value of the second image including the second image processed by the preset condition is less than the preset threshold;

blurring the processed second image to obtain a third image;

According to the second image, determine a third set of brightness values;

According to the third image, determine a fourth set of brightness values;

determining a second difference set according to the difference between the third set of luminance values and the fourth set of luminance values;

According to the second difference set, O difference values whose brightness values are greater than zero are determined to correspond to O second target pixels, where O is a positive integer less than N;

In the processed second image, remove 0 second target pixels to obtain a fourth image;

processing the fourth image based on the second curve;

The processed fourth image is updated to the image to be processed, and the step of blurring the image to be processed is performed again.

The embodiments of the present application can effectively brighten the dark areas in the face image, such as spots, scars and other areas, the processing will not enhance the face enhancement and brighten, so the three-dimensional structure of the face will not be damaged, and AI does not need to be used The algorithm, which occupies less equipment performance and costs less, realizes the uniformity of the brightness of the flawed part while maintaining the three-dimensional structure of the face, and improves the effect of skin resurfacing.

Optionally, the processor 510 is further configured to acquire a preset mask, where the preset mask is used to keep the brightness value of the second target area of the second image unchanged;

The second target area is processed by a preset mask.

In this embodiment of the present application, a preset mask is used to process the target area of the second image, that is, the facial features area. Under the protection of the preset mask, the brightness value in the facial features area will not be affected by the first curve and the second curve. , so the facial features of the face will not be affected by the microdermabrasion algorithm, and the details of the facial features can be accurately restored to ensure the microdermabrasion effect.

Optionally, the processor 510 is further configured to process the original image by using the second image to obtain the processed target image.

In the embodiment of the present application, after outputting the second image as the mask area after skin grinding, the mask area is processed with the original image, that is, the original face image, so as to brighten the dark areas in the face image, such as spots , scars and other areas to obtain the face image after microdermabrasion and beautification. This process will not brighten the face enhancement, so it will not damage the three-dimensional structure of the face, and does not need to use AI algorithms, the occupation and cost of equipment performance are small, and the three-dimensional structure of the face is maintained. It achieves the uniformity of the brightness of the flawed part and improves the effect of microdermabrasion.

It should be understood that, in this embodiment of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042. camera) to process the image data of still pictures or videos. The display unit 506 may include a display panel 5061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 5071 and other input devices 5072 . The touch panel 5071 is also called a touch screen. The touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which are not described herein again.

The memory 509 may be used to store software programs as well as various data. The memory 509 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 509 may include volatile memory or non-volatile memory, or memory 509 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 509 in this embodiment of the present application includes, but is not limited to, these and any other suitable types of memory.

The processor 510 may include one or more processing units; optionally, the processor 510 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 510.

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 image processing method embodiments, and can achieve the same technical effect , in order to avoid repetition, it will not be repeated 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 methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course hardware can also be used, 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 that are essentially or contribute to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk , CD), 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: get the original image; establishing an image pyramid corresponding to the original image; Determining the low-frequency map of the image pyramid as the image to be processed; performing a blurring process on the to-be-processed image to obtain a first image; generating a second image according to the to-be-processed image and the first image; determining a corresponding first curve according to the histogram distribution of the to-be-processed image, wherein the first curve is used to increase or decrease the brightness value of the first target area in the second image; processing the second image based on the first curve; When the processed second image satisfies a preset condition, the processed second image is output. 2 . The image processing method according to claim 1 , wherein the image to be processed includes N first pixels, the first image includes N second pixels, and the N first pixels are the same as the N first pixels. 3 . The N second pixels are in one-to-one correspondence, and N is a positive integer; The generating a second image according to the to-be-processed image and the first image includes: determining, according to the to-be-processed image, a first set of brightness values, where the set of first brightness values includes the brightness values of the N first pixels; determining, according to the first image, a second set of brightness values, the second set of brightness values including the brightness values of the N second pixels; The second image is generated from the first set of luminance values and the second set of luminance values. 3 . The image processing method according to claim 2 , wherein the generating the second image according to the first luminance value set and the second luminance value set comprises: 3 . determining a first difference set according to the difference between the first set of luminance values and the second set of luminance values; According to the M difference values whose luminance values are less than zero in the first difference value set, the corresponding M first target pixels are determined, where M is a positive integer less than N; The M first target pixels are removed from the to-be-processed image to obtain the second image. 4 . The image processing method according to claim 3 , wherein after processing the second image based on the first curve, the image processing method further comprises: 4 . In the case that the processed second image does not meet the preset condition, a corresponding second curve is determined according to the histogram distribution, wherein the second curve is used to increase or decrease the brightness of the first target area value, the preset condition includes that the intensity value of the processed second image is less than a preset threshold; blurring the processed second image to obtain a third image; determining a third set of luminance values according to the second image; determining a fourth set of luminance values according to the third image; determining a second difference set according to the difference between the third set of luminance values and the fourth set of luminance values; According to the second difference value set, O difference values whose brightness values are greater than zero are determined to correspond to O second target pixels, where O is a positive integer less than N; In the processed second image, remove the 0 second target pixels to obtain a fourth image; processing the fourth image based on the second curve; The processed fourth image is updated to the to-be-processed image, and the step of blurring the to-be-processed image is performed again. 5 . The image processing method according to claim 4 , wherein, before the second image is processed based on the first curve, or the second image is processed based on the second curve, the Methods also include: acquiring a preset mask, where the preset mask is used to keep the brightness value of the second target area of the second image unchanged; The second target area is processed by the preset mask. 6. The image processing method according to any one of claims 1 to 5, wherein after the output of the processed second image, the method further comprises: Through the second image, the original image is processed to obtain a processed target image. 7. An image processing device, comprising: The acquisition module is used to acquire the original image; establishing a module for establishing an image pyramid corresponding to the original image; A determination module, for determining the low-frequency map of the image pyramid as an image to be processed; a processing module, used for blurring the to-be-processed image to obtain a first image; a generating module, configured to generate a second image according to the to-be-processed image and the first image; The determining module is further configured to determine a corresponding first curve according to the histogram distribution of the to-be-processed image, wherein the first curve is used to increase or decrease the brightness of the first target area in the second image value; The processing module is further configured to process the second image based on the first curve; An output module, configured to output the processed second image if the processed second image satisfies a preset condition. 8 . The image processing apparatus according to claim 7 , wherein the image to be processed includes N first pixels, the first image includes N second pixels, and the N first pixels are the same as the N first pixels. 9 . The N second pixels are in one-to-one correspondence, and N is a positive integer; The determining module is further configured to determine a first brightness value set according to the to-be-processed image, where the first brightness value set includes the brightness values of the N first pixels; and determine the first brightness value according to the first image. Two sets of brightness values, the second set of brightness values includes the brightness values of the N second pixels; The generating module is further configured to generate the second image according to the first luminance value set and the second luminance value set. 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 The steps of any one of 1 to 6 of the method. 10. A readable storage medium, characterized in that, a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the method according to any one of claims 1 to 6 is implemented. step.

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