CN107580205A - Method and device for adjusting white balance - Google Patents

Abstract

The invention provides a white balance adjusting method and a white balance adjusting device, wherein the method comprises the following steps: calculating to obtain a first white balance gain value of the image by adopting a face white balance algorithm; calculating a plurality of second white balance gain values corresponding to the image if the image is obtained by imaging under a plurality of light sources respectively; selecting a target white balance gain value close to the first white balance gain value from the plurality of second white balance gain values according to the first white balance gain value; calculating to obtain a third white balance gain value of the image by adopting a simple gray world algorithm; and performing white balance adjustment on the image according to the target white balance gain value and the third white balance gain value. The technical problem that in the prior art, under the same scene, when a face exists in an image or the face does not exist in the image, the white balance gain value changes suddenly is solved.

Description

Method and device for adjusting white balance

technical field

The invention relates to the field of imaging technology, in particular to a white balance adjustment method and device.

Background technique

In related technologies, when using the shooting device of the terminal device to take pictures, there will be a deviation between the color value obtained by the actual color image collection and the real color of the object. There are two main reasons for this deviation. One is the color temperature of the light source environment Change, under different color temperature conditions, the reflected spectrum of the same object is different, resulting in different colors of the object under the light source of different color temperature, for example, a white object appears blue in a high color temperature environment, but in a low color temperature environment in red. The other is due to the deviation of the inherent color channel gain of the shooting device itself, for example, the manual gain value of the B channel of GC0307 is 0x98, while the manual gain value of the R and G channels is 0x80.

Therefore, in the related art, in order to compensate for this color deviation, the white balance gain value of the color gain channel of the shooting device is changed through the relevant white balance algorithm, and the color deviation caused by the color temperature environment and the inherent color channel of the shooting device itself Gain deviation is uniformly compensated, so that the acquired image can correctly reflect the true color of the object.

Among them, there are many kinds of white balance algorithms, all of which can be used to calculate the white balance gain value. In the portrait shooting scene, in order to achieve a better processing effect, different white balance algorithms are used for white balance based on whether there are faces or no faces. Balance processing, when taking pictures, in the same scene, when there is a face and no face, the obtained white balance gain value changes significantly, resulting in a sudden change in image color.

Contents of the invention

The present invention provides a white balance adjustment method and device to solve the technical problem in the prior art that, in the same scene, when there is a face and no face in the image, the white balance gain value changes suddenly, resulting in a color change.

An embodiment of the present invention provides a white balance adjustment method, which includes the following steps: using the face white balance algorithm to calculate the first white balance gain value of the image; A plurality of second white balance gain values corresponding to the image; according to the first white balance gain value, select from the plurality of second white balance gain values to obtain a target close to the first white balance gain value White balance gain value; a third white balance gain value of the image is calculated by using a simple grayscale world algorithm; according to the target white balance gain value of the target balance gain value and the third white balance gain value, the The image is white balance adjusted.

Another embodiment of the present invention provides a white balance adjustment device, including: a first calculation module, used to calculate the first white balance gain value of an image by using a face white balance algorithm; a second calculation module, used to calculate if When the image is obtained by imaging under a variety of light sources, a plurality of second white balance gain values corresponding to the image; a selection module, configured to select from the plurality of second white balance gain values according to the first white balance gain value A target white balance gain value close to the first white balance gain value is selected from the white balance gain value; the third calculation module is used to calculate the third white balance gain value of the image by using a simple grayscale world algorithm ; An adjustment module, configured to adjust the white balance of the image according to the target white balance gain value and the third white balance gain value.

Yet another embodiment of the present invention provides a computer device, including a memory and a processor. Computer-readable instructions are stored in the memory. When the instructions are executed by the processor, the processor executes the above-mentioned embodiment of the present invention. The white balance adjustment method described in the example.

Another embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the white balance adjustment method as described in the above-mentioned embodiments of the present invention is implemented.

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

Use the face white balance algorithm to calculate the first white balance gain value of the image, and calculate the multiple second white balance gain values corresponding to the image when the image is imaged under multiple light sources, according to the first white balance gain Value, select the target white balance gain value close to the first white balance gain value from multiple second white balance gain values, and use the simple grayscale world algorithm to calculate the third white balance gain value of the image, according to the target white balance gain value The balance gain value and the third white balance gain value are used to adjust the white balance of the image. As a result, the problem of flickering of the screen caused by sudden changes in the white balance gain value when there is a human face or no human face in the same scene is suppressed, and damage to human eyes is avoided.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and easy to understand from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

FIG. 1 is a flowchart of a white balance adjustment method according to an embodiment of the present invention;

FIG. 2 is a flowchart of a white balance adjustment method according to another embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a white balance adjustment device according to an embodiment of the present invention; and

FIG. 4 is a schematic structural diagram of an image processing circuit in a computer device according to an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

It can be understood that in many application scenarios in practical applications, the user uses an application program in a terminal device such as a smart phone to take pictures, wherein, taking pictures in a portrait mode such as a front camera mode or a portrait camera mode of the terminal device, and , when taking photos in non-portrait shooting modes such as the rear camera mode, the white balance algorithm used is different, because the color composition of the image in the face shooting mode and the non-face shooting mode is different. Specifically, in the portrait mode, the Face Automatic White Balance (FaceAWB) algorithm is adopted. When there are people in the image, the skin color of a class of people changes very little, within an estimable range. Therefore, the corresponding correction operator can be determined according to the characteristics of the skin color of the human face, so as to obtain a more accurate white balance calculation result. It can effectively improve the white balance effect of the image, especially under the condition of large-area solid color background and/or mixed light.

In the non-portrait mode, the Simple Gray World algorithm is used. The Simple Gray World algorithm is based on the gray world assumption. The average value of the saturation of the three components of Red, R), green (Green, G) and blue (Blue, B) tends to the same gray value. That is, the grayscale world algorithm assumes that the mean value of the average reflection of light by natural scenes is generally a fixed value, and the saturation of the three components of R, G, and B in this fixed value tends to be consistent. When there are rich colors in the image, the effect of ambient light can be better eliminated by processing the image through the grayscale world algorithm.

However, the white balance gain values obtained by using different white balance algorithms vary greatly. In the same application scenario, when the terminal device moves from a scene with a face to a scene without a face, the obtained white balance gain If there is a big difference in the value, it will lead to a sudden change in the color, which is harmful to the human eye and the visual effect is not good.

In order to solve the above technical problems, the present invention proposes a white balance adjustment method and device, which can suppress the sudden change of the white balance gain value.

The method and device for adjusting white balance according to the embodiments of the present invention will be described below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a method for adjusting white balance according to an embodiment of the present invention. As shown in Fig. 1, the method includes the following steps:

Step 101, using a face white balance algorithm to calculate a first white balance gain value of the image.

Specifically, in order to select an appropriate gain value for the face image to perform white balance processing on the image, so that in the image processing result, the color of the face is more consistent with the skin color, the image can be calculated according to the face white balance algorithm to obtain the first Gain value for backup.

Step 102, calculating a plurality of second white balance gain values corresponding to the image if the image is obtained by imaging under various light sources.

Among them, the light source includes: one or more combinations of daylight light source, fluorescent light source, tungsten light source and F-A-H light source, wherein, the F-A-H light source is a light source between A light and H light, the color temperature of A light is 2850K, and the color temperature of H light is 2850K. for 2350K.

Specifically, in order to select an appropriate gain value for the non-face image to perform white balance processing on the image, so that the color of the non-face area in the image processing result is more consistent with the natural color, calculate if the image obtained by imaging under various light sources In this case, multiple second white balance gain values respectively corresponding to the images are used for backup, and the second white balance gain values are relatively close to the algorithm results of the grayscale world.

Step 103, according to the first white balance gain value, select a target white balance gain value close to the first white balance gain value from multiple second white balance gain values.

Specifically, in order to avoid large differences in white balance gain values in the face-photographing mode and non-face-photographing mode, according to the first white balance gain value, a plurality of second white balance gain values are selected to obtain the same white balance gain value as the first white balance gain value. The target white balance gain value is close to the balance gain value. Therefore, according to the target white balance gain value, on the one hand, the skin color of the human face is considered, and on the other hand, the rich colors in nature (gray world) are considered, which can not only improve the visual effect of image processing Effect, and based on the target white balance gain value is relatively close to the white balance gain value in the grayscale world, it avoids the problem of screen flicker caused by sudden changes in the white balance gain value.

It should be noted that, according to different application scenarios, different implementation methods can be adopted. According to the first white balance gain value, a target white balance close to the first white balance gain value can be obtained by selecting from multiple second white balance gain values. The gain value is illustrated as follows:

The first example, determine the first gain value of each color component in the first white balance gain value, determine the second gain value of each color component for each second white balance gain value, and determine the second gain value of each color component for each second white balance gain value The difference between the gain value and the first white balance gain value is calculated. The difference value is calculated for the absolute difference of each color component after calculating the absolute difference between the first gain value and the second gain value in the same color component. and obtained, from the multiple second white balance gain values, select the target white balance gain value with the smallest difference from the first gain value.

In the second example, the first vector is generated according to the first gain value of the first white balance gain value on each color component, and the corresponding vector is generated according to the second gain value of each second white balance gain value on each color component. multiple second vectors, calculate the vector distance between the first vector and each second vector, the vector distance includes the Euclidean distance, according to the vector distance, select the vector from multiple second white balance gain values The target white balance gain value with the smallest distance.

In step 104, a third white balance gain value of the image is calculated by using a simple grayscale world algorithm.

It should be understood that the grayscale world algorithm is based on the assumption that for an image with a large number of color changes, the average value of the saturation of the three components R, G, and B tends to the same grayscale value G. In practical applications, there are usually two methods to determine the gray value G.

As a possible implementation manner, a fixed value can be taken. For example, half of the brightest grayscale value can be taken, that is, when the brightest grayscale value is 255, the grayscale value G can be 128.

As another possible implementation manner, the average value of the three colors R, G, and B in the image may be calculated, and the average value of these three average values may be taken as the gray value G. After the grayscale value G is determined, the second gain value of the image can be calculated by comparing the grayscale value G with the average values of the three colors R, G, and B respectively.

Step 105, adjust the white balance of the image according to the target white balance gain value and the third white balance gain value.

Specifically, since the target white balance value is a gain value with a smaller difference from the first gain value, the processing effect on the face may be considered to a greater extent. When switching from a scene containing a face to a scene not containing a face, It may be considered that a sudden change in color occurs due to a large change in color composition. Therefore, in the embodiment of the present invention, further considering the target white balance gain value and the third white balance gain value based on the grayscale world calculation, the The white balance of the image is adjusted so that when switching from a scene containing a human face to a scene not containing a human face, the white balance gain value is closer to the white balance gain value of the grayscale world, effectively avoiding the sudden change of the white balance gain value. The color change problem.

It should be noted that, according to different application scenarios, different implementation methods can be used to adjust the white balance of the image according to the target white balance gain value and the third white balance gain value. Examples are as follows:

First example:

Calculate the weighted average value of the target white balance gain value and the third white balance gain value, and adjust the white balance of the image according to the weighted average value.

Second example:

According to the area proportion of the face area in the image, determine the weight of the target white balance gain value and the weight of the third white balance gain value, wherein, because the proportion of the face area is larger, the target white balance gain value and the grayscale world The greater the difference in the third white balance gain value, therefore, in order to ensure that the primary color of the captured face area matches the skin color, the weight of the target white balance gain value and the area ratio are set to have a positive relationship. The weight and the weight of the third white balance gain value are calculated to obtain the weighted average.

To sum up, the white balance adjustment method of the embodiment of the present invention uses the face white balance algorithm to calculate the first white balance gain value of the image, and calculates the corresponding gain value of the image if the image is obtained by imaging under various light sources. Multiple second white balance gain values, according to the first white balance gain value, select from multiple second white balance gain values to obtain a target white balance gain value close to the first white balance gain value, using a simple grayscale world algorithm , calculate the third white balance gain value of the image, and adjust the white balance of the image according to the target white balance gain value and the third white balance gain value. As a result, the problem of flickering of the screen caused by sudden changes in the white balance gain value when there is a human face or no human face in the same scene is suppressed, and damage to human eyes is avoided.

Based on the above embodiments, in order to further describe in detail, how to select a target white balance gain value close to the first white balance gain value from multiple second white balance gain values according to the first white balance gain value, the following combines the above The determination of the target white balance gain value based on the vector of gain values shown in the second example is taken as an example for description.

Fig. 2 is a flowchart of a method for adjusting white balance according to another embodiment of the present invention. As shown in Fig. 2, the method includes:

Step 201, using a face white balance algorithm to calculate a first white balance gain value of the image.

Specifically, face recognition can be performed on the image through face recognition technology to determine that the image contains a face area. Coordinate interval, wherein, the face recognition algorithm, there are many ways of implementation in the prior art, for example, adopt Adaboost model algorithm to carry out face recognition, can also adopt other algorithm that can identify face area quickly, carry out face area identify. The implementation manner of corresponding face recognition is not limited in this embodiment.

After getting the face area, the skin color of a class of people changes very little. For example, according to statistics, after the skin color RGB color space is converted to the YCbCr space, the Cb and Cr ranges of the face are [133, 173], [77, 127] respectively. That is, as long as the skin color range of a person can be determined, the image can be corrected according to the skin color range. Therefore, the first gain value of the image can be calculated by comparing the color of the face area in the image with the preset range of skin color.

Of course, the purpose of face recognition to determine the first gain value in the above embodiment is to obtain the first gain value when performing white balance processing based on skin color in the face photographing mode. In fact, in the front camera photographing mode or in the In the camera mode of the rear camera, the white balance processing is performed based on the face white balance algorithm. Therefore, it can also be determined that when the image is imaged by the front camera, the face white balance algorithm is used to calculate the first white balance gain of the image. value, or, when the image is determined to be imaged in the portrait mode of the rear camera, the face white balance algorithm is used to calculate the first white balance gain value of the image, etc.

Step 202 , calculating a plurality of second white balance gain values corresponding to the image if the image is obtained by imaging under various light sources.

Step 203: Generate a first vector according to the first gain value of the first white balance gain value on each color component.

Step 204: Generate a plurality of corresponding second vectors according to the second gain value of each second white balance gain value on each color component.

In practical applications, the first gain value and the second gain value can be accurately represented by vectors in a color space. There are many kinds of color spaces, for example: RGB (red, green, blue) color space, that is, a color space based on three primary colors of a device. In addition, it may also be an HSI color space, which starts from the human visual system and uses hue (Hue), color saturation (Saturation or Chroma) and brightness (Intensity or Brightness) to describe colors. The HSI color space can be described by a cone space model. Of course, other color spaces may also be used for description, which will not be repeated in this embodiment. As a possible implementation manner, an RGB model in a color space may be used to characterize the first gain value and the second gain value.

Specifically, in the RGB model, each color appears in three color components of R, G, and B. This model is based on a Cartesian coordinate system, and the considered color space is a cube. One vertex of the cube could be the origin, with black at that origin and white at the vertex in the cube that is farthest from the origin. In this model, the different colors are on or inside the cube and can be represented by vectors distributed from the origin.

As a possible implementation, assuming that all colors are normalized, the cube is a unit cube, that is, all values of R, G, and B are within the range of [0, 1]. Therefore, the values of the first gain value and the second gain value on each color component of R, G, and B may also be within the range of [0, 1]. The first vector can be generated by combining the values of the first gain value on each color component, and the second vector can be generated by combining the values of the second gain value on each color component. For example, if the value of the first gain value on the R component is 0.1, the value on the G component is 0.2, and the value on the B component is 0.3, then each color component can be The value of , generate the first vector [0.1, 0.2, 0.3]. If the value of the second gain value on the R component is 0.2, the value on the G component is 0.2, and the value on the B component is 0.2, then according to the value of the second gain value on each color component value, generating the first vector [0.2, 0.2, 0.2].

Step 205, calculate the vector distance between the first vector and each second vector; the vector distance includes Euclidean distance.

Specifically, after the first vector and the second vector are generated, the quantitative representation of the first gain value and the second gain value is realized. When calculating the vector distance between the first vector and the second vector, the Euclidean distance can be used to describe the vector distance between the two vectors, or the cosine distance, Pearson correlation coefficient, etc. can be used to describe the two Vector distance between vectors. Taking the Euclidean distance to describe the vector distance between the first vector and the second vector as an example, the following Euclidean distance formula can be used:

Computes the vector distance between the first vector and the second vector. Among them, d(x, y) is the vector distance between the first vector and the second vector, x _R , x _G , x _B are the values of each color component in the first vector, y _R , y _G , y _B are the values of each color component in the second vector, respectively.

Furthermore, after the vector distance between the first vector and the second vector is calculated, the vector distance between the first vector and the second vector can be judged, the more the vector distance between the first vector and the second vector The larger the value, the more dissimilar the first gain value and the second gain value can be determined. If the vector distance between the first vector and the second vector is closer, it can be determined that the first gain value and the second gain value are similar.

Step 206, according to the vector distance, select the target white balance gain value with the smallest vector distance from the plurality of second white balance gain values.

In step 207, a third white balance gain value of the image is calculated by using a simple grayscale world algorithm.

Step 208, adjust the white balance of the image according to the target white balance gain value and the third white balance gain value.

Specifically, according to the vector distance, from the multiple second white balance gain values, select the target white balance gain value with the smallest vector distance, and combine the third white balance gain value and the target white balance gain value calculated by the grayscale world algorithm Accurate white balance processing of images, on the one hand, considers the skin color of the face, and on the other hand, considers the rich colors of nature, which can not only improve the visual effect of image processing, but also avoid the problem of screen flicker caused by sudden changes in white balance gain.

To sum up, in the white balance adjustment method of the embodiment of the present invention, according to the face white balance algorithm used to adjust the face in the image to the skin color, calculate the first gain value for the image, and calculate if the When an image is obtained by imaging under such a light source, a plurality of second white balance gain values corresponding to the image respectively generate a first vector according to the first gain value of the first white balance gain value on each color component, and according to each second The second gain value of the white balance gain value on each color component generates a plurality of corresponding second vectors, and calculates the vector distance between the first vector and each second vector; the vector distance includes the Euclidean distance, According to the vector distance, select the target white balance gain value with the smallest vector distance from multiple second white balance gain values, and then use the simple grayscale world algorithm to calculate the third white balance gain value of the image. The balance gain value and the third white balance gain value are used to adjust the white balance of the image. Therefore, in conjunction with the slower white balance convergence speed, it can effectively improve the problem of flickering when there is no face when using the face white balance algorithm for white balance adjustment.

In order to realize the above-mentioned embodiment, the present invention also proposes a white balance adjustment device. FIG. 3 is a schematic structural diagram of a white balance adjustment device according to an embodiment of the present invention. As shown in FIG. 3 , the white balance adjustment device includes a first calculation Module 100 , second calculation module 200 , selection module 300 , third calculation module 400 and adjustment module 500 .

Wherein, the first calculating module 100 is configured to calculate a first white balance gain value of the image by using a face white balance algorithm.

The second calculation module 200 is configured to calculate multiple second white balance gain values corresponding to the image if the image is obtained by imaging under multiple light sources.

A selection module 300, configured to select a target white balance gain value close to the first white balance gain value from a plurality of second white balance gain values according to the first white balance gain value;

The third calculation module 400 is configured to calculate a third white balance gain value of the image by using a simple grayscale world algorithm.

The adjustment module 500 is configured to adjust the white balance of the image according to the target white balance gain value and the third white balance gain value.

It should be noted that the foregoing descriptions of the method embodiments are also applicable to the devices of the embodiments of the present invention, and their implementation principles are similar, so details are not repeated here.

The division of each module in the above-mentioned white balance adjustment device is only for illustration. In other embodiments, the white balance adjustment device can be divided into different modules as required to complete all or part of the functions of the above-mentioned white balance adjustment device.

To sum up, the white balance adjustment device of the embodiment of the present invention uses the face white balance algorithm to calculate the first white balance gain value of the image, and calculates the corresponding gain value of the image if the image is obtained by imaging under various light sources. Multiple second white balance gain values, according to the first white balance gain value, select from multiple second white balance gain values to obtain a target white balance gain value close to the first white balance gain value, using a simple grayscale world algorithm , calculate the third white balance gain value of the image, and adjust the white balance of the image according to the target white balance gain value and the third white balance gain value. As a result, the problem of flickering of the screen caused by sudden changes in the white balance gain value when there is a human face or no human face in the same scene is suppressed, and damage to human eyes is avoided.

To achieve the above object, an embodiment of the present invention further provides a computer device. The above computer device includes an image processing circuit, which may be implemented by hardware and/or software components, and may include various processing units defining an ISP (Image Signal Processing, image signal processing) pipeline. Fig. 4 is a schematic diagram of an image processing circuit in one embodiment. As shown in FIG. 4 , for ease of description, only various aspects of the image processing technology related to the embodiment of the present invention are shown.

As shown in FIG. 4 , the image processing circuit includes an ISP processor 1040 and a control logic 1050 . Image data captured by imaging device 1010 is first processed by ISP processor 1040 , which analyzes the image data to capture image statistics that may be used to determine and/or control one or more parameters of imaging device 1010 . Imaging device 1010 may include a camera having one or more lenses 1012 and an image sensor 1014 . Image sensor 1014 may include a color filter array (such as a Bayer filter), and image sensor 1014 may obtain light intensity and wavelength information captured with each imaging pixel of image sensor 1014 and provide a set of raw images that may be processed by ISP processor 1040. image data. The sensor 1020 may provide raw image data to the ISP processor 1040 based on the sensor 1020 interface type. The interface of the sensor 1020 may utilize a SMIA (Standard Mobile Imaging Architecture, Standard Mobile Imaging Architecture) interface, other serial or parallel camera interfaces, or a combination of the above interfaces.

The ISP processor 1040 processes raw image data pixel by pixel in various formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and the ISP processor 1040 may perform one or more image processing operations on raw image data, collect statistical information about the image data. Among other things, image processing operations can be performed with the same or different bit depth precision.

ISP processor 1040 may also receive pixel data from image memory 1030 . For example, the raw pixel data is sent from the sensor 1020 interface to the image memory 1030, and the raw pixel data in the image memory 1030 is provided to the ISP processor 1040 for processing. The image memory 1030 may be a part of a memory device, a storage device, or an independent dedicated memory in an electronic device, and may include a DMA (Direct Memory Access, direct memory access) feature.

Upon receiving raw image data from the sensor 1020 interface or from the image memory 1030, the ISP processor 1040 may perform one or more image processing operations, such as temporal filtering. The processed image data may be sent to image memory 1030 for additional processing before being displayed. The ISP processor 1040 receives the processed data from the image memory 1030 and performs image data processing in the original domain and in the RGB and YCbCr color spaces on the processed data. The processed image data can be output to the display 1070 for viewing by the user and/or further processed by a graphics engine or a GPU (Graphics Processing Unit, graphics processor). In addition, the output of the ISP processor 1040 can also be sent to the image memory 1030 , and the display 1070 can read image data from the image memory 1030 . In one embodiment, image memory 1030 may be configured to implement one or more frame buffers. Also, the output of the ISP processor 1040 may be sent to an encoder/decoder 1060 for encoding/decoding image data. The encoded image data may be saved and decompressed prior to display on the display 1070 device. Encoder/decoder 1060 may be implemented by a CPU or GPU or a coprocessor.

The statistics determined by the ISP processor 1040 may be sent to the control logic 1050 unit. For example, statistical data may include image sensor 1014 statistical information such as auto exposure, auto white balance, auto focus, flicker detection, black level compensation, lens 1012 shading correction, and the like. Control logic 1050 may include a processor and/or a microcontroller that executes one or more routines (e.g., firmware) that determine control parameters of imaging device 1010 and control of imaging device 1010 based on received statistical data. parameter. For example, control parameters may include sensor 1020 control parameters (eg, gain, integration time for exposure control), camera flash control parameters, lens 1012 control parameters (eg, focal length for focus or zoom), or combinations of these parameters. ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (eg, during RGB processing), as well as lens 1012 shading correction parameters.

The following are the steps of implementing the white balance adjustment method using the image processing technology in Fig. 4:

Step 101', using the face white balance algorithm to calculate the first white balance gain value of the image.

Step 102', calculating multiple second white balance gain values corresponding to the image if the image is obtained by imaging under multiple light sources.

Step 103', according to the first white balance gain value, select a target white balance gain value close to the first white balance gain value from the plurality of second white balance gain values.

Step 104', using a simple grayscale world algorithm to calculate the third white balance gain value of the image.

Step 105', adjust the white balance of the image according to the target white balance gain value and the third white balance gain value.

It should be noted that the foregoing explanations of the method embodiments are also applicable to the terminal device of this embodiment, and the implementation principles thereof are similar, and details are not repeated here.

To sum up, the terminal device of the embodiment of the present invention adopts the face white balance algorithm to calculate the first white balance gain value of the image, and calculates the multiple white balance gain values corresponding to the image when the image is obtained by imaging under multiple light sources. The second white balance gain value, according to the first white balance gain value, is selected from multiple second white balance gain values to obtain a target white balance gain value close to the first white balance gain value, using a simple grayscale world algorithm to calculate A third white balance gain value of the image is obtained, and white balance adjustment is performed on the image according to the target white balance gain value and the third white balance gain value. As a result, the problem of flickering of the screen caused by sudden changes in the white balance gain value when there is a human face or no human face in the same scene is suppressed, and damage to human eyes is avoided.

The embodiment of the present invention also proposes a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the white balance adjustment method as described in the foregoing embodiments can be implemented.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless specifically defined otherwise.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing custom logical functions or steps of a process , and the scope of preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order depending on the functions involved, which shall It is understood by those skilled in the art to which the embodiments of the present invention pertain.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium, For use with instruction execution systems, devices, or devices (such as computer-based systems, systems including processors, or other systems that can fetch instructions from instruction execution systems, devices, or devices and execute instructions), or in conjunction with these instruction execution systems, devices or equipment used. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. The program is processed electronically and stored in computer memory.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented by any one or a combination of the following techniques known in the art: a discrete Logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGA), Field Programmable Gate Arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. During execution, one or a combination of the steps of the method embodiments is included.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, those skilled in the art can make the above-mentioned The embodiments are subject to changes, modifications, substitutions and variations.

Claims (10)

1. a kind of white balance adjustment method, it is characterised in that comprise the following steps：

Using face white balance algorithm, the first white balance gains value of image is calculated；

If calculating respectively, when obtaining described image, multiple second white balances corresponding to described image increase for imaging under various light sources Benefit value；

According to the first white balance gains value, chosen from the multiple second white balance gains value obtain with it is described first white The close target white balance gains value of balancing gain value；

Using simple gray world algorithm, the 3rd white balance gains value of described image is calculated；

According to the target white balance gains value and the 3rd white balance gains value, blank level adjustment is carried out to described image.

2. white balance adjustment method according to claim 1, it is characterised in that described according to first white balance gains Value, chosen from the multiple second white balance gains value and obtain the target white balance close with the first white balance gains value Yield value, including：

Determine in the first white balance gains value, the first yield value of each color component；

For each the second white balance gains value, the second yield value of each color component is determined；

Difference value between each second white balance gains value and the first white balance gains value is calculated, the difference Different value is after calculating the absolute difference of the first yield value described in same color component and second yield value, to each color point The absolute difference of amount sums what is obtained；

From multiple second white balance gains values, the target white balance the difference value minimum between the first yield value is chosen Yield value.

3. white balance adjustment method according to claim 1, it is characterised in that described according to first white balance gains Value, chosen from the multiple second white balance gains value and obtain the target white balance close with the first white balance gains value Yield value, including：

According to first yield value of the first white balance gains value on each color component, primary vector is generated；

According to each second yield value of the second white balance gains value on each color component, corresponding to generation multiple second to Amount；

Calculate the vector distance between the primary vector and each described secondary vector；The vector distance include Europe it is several in Obtain distance；

According to the vector distance, from the multiple second white balance gains value, choose and obtain the vector distance minimum Target white balance gains value.

4. according to the white balance adjustment method described in claim any one of 1-3, it is characterised in that described to use face white balance Algorithm, it is calculated before the first white balance gains value of image, in addition to：

Recognition of face is carried out to described image, to determine to include human face region in described image；

Or, determine that described image is imaged to obtain using front camera；

Or, determine that described image is imaged to obtain using the portrait mode of figure of rear camera.

5. according to the white balance adjustment method described in claim any one of 1-3, it is characterised in that the light source includes：Daylight One or more of light source, fluorescence light source, tungsten filament lamp sources and F-A-H light sources combine.

6. according to the white balance adjustment method described in claim any one of 1-3, it is characterised in that described white according to the target Balancing gain value and the 3rd white balance gains value, blank level adjustment is carried out to described image, including：

Calculate the weighted average of the target white balance gains value and the 3rd white balance gains value；

According to the weighted average, blank level adjustment is carried out to described image.

7. white balance adjustment method according to claim 6, it is characterised in that described to calculate the target white balance gains The weighted average of value and the 3rd white balance gains value, including：

According to the area accounting of human face region in described image, the weight and the described 3rd of the target white balance gains value is determined The weight of white balance gains value；Wherein, it is positive pass between the weight of the target white balance gains value and the area accounting System；

According to the weight of the target white balance gains value and the weight of the 3rd white balance gains value, described add is calculated Weight average value.

A kind of 8. white balance adjustment device, it is characterised in that including：

First computing module, for using face white balance algorithm, the first white balance gains value of image is calculated；

Second computing module, if for when calculating that imaging obtains described image under various light sources respectively, corresponding to described image Multiple second white balance gains values；

Module is chosen, for according to the first white balance gains value, being chosen from the multiple second white balance gains value To the target white balance gains value close with the first white balance gains value；

3rd computing module, for using simple gray world algorithm, the 3rd white balance gains value of described image is calculated；

Adjusting module, for according to the target white balance gains value and the 3rd white balance gains value, entering to described image Row blank level adjustment.

9. a kind of computer equipment, it is characterised in that including memory, processor and storage on a memory and can be in processor The computer program of upper operation, during the computing device described program, realize putting down in vain as described in any in claim 1-7 Weigh method of adjustment.

10. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the program is by processor The white balance adjustment method as described in any in claim 1-7 is realized during execution.