CN112218065B - Image white balance method, system, terminal device and storage medium - Google Patents
Image white balance method, system, terminal device and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/84—Camera processing pipelines; Components thereof for processing colour signals
- H04N23/88—Camera processing pipelines; Components thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/71—Circuitry for evaluating the brightness variation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/76—Circuitry for compensating brightness variation in the scene by influencing the image signals
Abstract
The application provides an image white balance method, a system, a terminal device and a storage medium, wherein the method comprises the following steps: determining the illumination type of the current environment according to the histogram information of the Bayer image of the current environment of the camera; if the illumination type of the current environment is a low illumination type, calculating an image gray value according to a three-channel color histogram of an ROI (region of interest) in the Bayer image; if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI; and adjusting the illumination intensity of the light supplementing lamp to the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state. This application carries out the mode of light filling through adopting the light filling lamp to Bayer image for Bayer image is in white balance state, and because do not relate to the zoom of lens light ring, so does not cause the reduction of degree of depth of field, has ensured the image display scope of Bayer image.
Description
Technical Field
The present application relates to the field of image processing, and in particular, to a method, a system, a terminal device, and a storage medium for white balance of an image.
Background
When the camera is used for shooting in a complex environment, the pictures shot by the camera are often easily influenced by ambient light, so that the shot pictures deviate from the original color of an object. For example, under the irradiation of a fluorescent lamp, the color of an object in a photographed picture is greenish; under the irradiation of the tungsten lamp, the color of the object in the shot picture is yellow. The white balance processing is processing for correcting colors in the picture so that an object in the picture presents an original color.
In the existing image white balance operation process, an original image (Bayer image) obtained by photographing of a camera is in a white balance state by adopting a mode of enlarging a lens aperture under a low-illumination environment, but the depth of field is reduced due to the fact that the lens aperture is enlarged, and the image display range of the Bayer image is further reduced.
Disclosure of Invention
The embodiment of the application provides an image white balance method, an image white balance system, terminal equipment and a storage medium, and aims to solve the problem that a Bayer image display range is small due to the fact that a lens aperture is enlarged to achieve an image white balance effect in the existing image white balance process.
In a first aspect, an embodiment of the present application provides an image white balance method, where the method includes:
acquiring histogram information of a Bayer image of a camera in a current environment, and determining the illumination type of the current environment according to the histogram information, wherein the histogram information comprises a three-channel color histogram and a gray level histogram, and the illumination type is used for representing the illumination intensity of the current environment;
if the illumination type of the current environment is a low illumination type, acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
and adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
Compared with the prior art, the embodiment of the application has the advantages that: the method for supplementing light to the Bayer image through the light supplementing lamp is adopted, the Bayer image is in a white balance state, the zoom of a lens aperture is not involved, the reduction of the depth of field is not caused, the image display range of the Bayer image is guaranteed, the illumination type of the current environment is determined according to the histogram information, whether the light supplementing lamp needs to be adopted to supplement light to the Bayer image is judged, the illumination adjusting value is calculated according to the brightness information of the ROI area, the adjusting value of the light supplementing lamp to the illumination intensity of the Bayer image is calculated, and the white balance effect of the Bayer image is improved.
Further, after determining the illumination type of the current environment according to the three-channel color histogram of the Bayer image, the method further includes:
if the illumination type of the current environment is a normal illumination type, starting a white balance mode in the camera, and acquiring a shot image of the camera in the white balance mode;
carrying out light color cast detection on the current environment according to the shot image;
if the light of the current environment has color cast, closing the white balance mode, and judging that the illumination type of the current environment is the low illumination type;
acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
and adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
Further, the determining the illumination type of the current environment according to the three-channel color histogram of the Bayer image includes:
calculating a histogram average value according to a three-channel color histogram and a gray level histogram of the Bayer image, and calculating a histogram standard deviation according to the histogram average value and the gray level histogram;
if the mean value of the histogram is smaller than a brightness threshold value and the standard deviation of the histogram is smaller than a variance threshold value, judging that the illumination type of the current environment is the low illumination type;
if the histogram average value is larger than or equal to a brightness threshold value, judging that the illumination type of the current environment is the normal illumination type;
and if the standard deviation of the histogram is larger than or equal to a variance threshold value, judging that the illumination type of the current environment is the normal illumination type.
Further, the calculating the image gray value of the Bayer image according to the three-channel color histogram of the ROI region includes:
respectively calculating image gray values corresponding to a red light channel, a green light channel and a blue light channel in the ROI according to the three-channel color histogram of the ROI and the following formula:
wherein, AvecrThe image gray value, Ave, corresponding to the red light channel in the ROI areacgThe image gray value, Ave, corresponding to the green light channel in the ROI areacbThe image gray value H corresponding to the blue light channel in the ROI areacrA color histogram, H, corresponding to a red light channel in the ROI areacgA color histogram, H, corresponding to the green channel in the ROI areacbAnd obtaining a color histogram corresponding to the blue light channel in the ROI area.
Further, after the calculating the gray values of the images corresponding to the red light channel, the green light channel and the blue light channel in the ROI region respectively, the method further includes:
if the gray values of the images corresponding to the red light channel, the green light channel and the blue light channel in the ROI area are all within a preset brightness value range, judging that the gray values of the images meet the preset white balance condition;
and if the image gray value corresponding to any one of the red light channel, the green light channel and the blue light channel in the ROI area is not in the preset brightness value range, judging that the image gray value does not meet the preset white balance condition.
Further, a calculation formula adopted for calculating the illumination adjustment value according to the brightness information of the ROI area is as follows:
wherein u isr(m) is a picture for the mth frame in the Bayer imageIllumination adjustment value u of image red light supplement lampg(m) is an illumination adjustment value of a green fill light for the mth frame image in the Bayer image, ub(m) is an illumination adjustment value of a blue light supplementary lamp for the mth frame image in the Bayer image, Kp、KiAnd KdRespectively being proportional term, differential term and integral term coefficient in PID algorithm, e (m) being brightness difference value between average brightness value and preset brightness value of mth frame image in Bayer image.
Further, the adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjustment value so that the Bayer image is in a white balance state further includes:
obtaining an ROI (region of interest) area of any appointed frame image in the Bayer image, and calculating a channel brightness mean value of the ROI area of the appointed frame image to obtain a current frame channel brightness mean value;
obtaining an ROI (region of interest) area of a previous frame image of the appointed frame image, and calculating a channel brightness mean value of the ROI area of the previous frame image to obtain a channel brightness mean value of the previous frame;
calculating a brightness mean value difference value between the brightness mean value of the current frame channel and the brightness mean value of the previous frame channel, and calculating a steady-state parameter in a pool type function according to the brightness mean value difference value;
and if the steady-state parameter is a preset parameter value, determining that the white balance performance of the image of the Bayer image is stable.
In a second aspect, an embodiment of the present application provides an image white balance system, including:
the illumination type determining module is used for acquiring histogram information of a Bayer image of a camera in a current environment, and determining an illumination type of the current environment according to the histogram information, wherein the histogram information comprises a three-channel color histogram and a gray level histogram, and the illumination type is used for representing the illumination intensity of the current environment;
the gray value calculation module is used for acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image if the illumination type of the current environment is a low illumination type, and calculating the image gray value of the Bayer image according to the three-channel color histogram of the ROI;
the adjusting value calculating module is used for calculating an illumination adjusting value according to the brightness information of the ROI if the gray value of the image does not meet a preset white balance condition;
and the light supplement lamp adjusting module is used for adjusting the light supplement lamp to the illumination intensity of the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the method described above.
In a fourth aspect, the present application provides a storage medium storing a computer program, and when the computer program is executed by a processor, the computer program implements the method as described above.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the image white balance method according to any one of the first aspect.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below.
Fig. 1 is a flowchart of an image white balance method according to a first embodiment of the present application;
FIG. 2 is a flowchart of an image white balance method according to a second embodiment of the present application;
FIG. 3 is a flowchart of an image white balance method according to a third embodiment of the present application;
FIG. 4 is a flowchart of an image white balance method according to a fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of an image white balance system provided in a fifth embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to a sixth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Example one
Please refer to fig. 1, which is a flowchart illustrating a method for white balance of an image according to a first embodiment of the present application, including the steps of:
step S10, acquiring histogram information of a Bayer image of a camera in the current environment, and determining the illumination type of the current environment according to the histogram information;
the histogram information comprises a three-channel color histogram and a gray level histogram, the three-channel color histogram comprises the proportion of different colors in the color of the whole image under different channels in the Bayer image, and the gray level histogram comprises the occurrence frequency of corresponding gray values of different pixel points in the Bayer image.
Specifically, in this step, the three-channel color histogram includes a red-channel color histogram, a green-channel color histogram, and a blue-channel color histogram, and the three-channel color histogram is obtained by calculating the number of pixels of corresponding pixels in a red channel, a green channel, and a blue channel in the Bayer image, and calculating the proportions of red, green, and blue in the image color of the Bayer image based on the number of pixels.
Optionally, in this step, gray level calculation is performed on each pixel point in the Bayer image to obtain a plurality of gray levels, and frequencies of different gray levels are counted to obtain the gray level histogram.
In addition, in this step, the illumination type of the current environment is determined according to the proportion of red, green and blue in the three-channel color histogram and the frequency of different gray values in the gray histogram, where the illumination type is used to represent the illumination intensity of the current environment.
Step S20, if the illumination type of the current environment is a low illumination type, acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
if the illumination type of the current environment is a low illumination type, it is determined that the illumination intensity of the current environment is low, and it is necessary to implement the white balance method in this embodiment to perform white balance operation on the Bayer image, and the ROI region (region of interest) may be obtained by setting a white calibration object in the current environment, where the ROI region is a region image of the white calibration object in the Bayer image, and therefore, in this step, the ROI region is extracted based on the image position by obtaining the image position of the white calibration object in the Bayer image, and the proportions of red, green, and blue in the image color of the ROI region are calculated respectively under a red channel, a green channel, and a blue channel, so as to obtain a corresponding three-channel color histogram.
In addition, in this step, if the illuminance type of the current environment is not the low illuminance type, it is determined that the illuminance type of the current environment is the normal illuminance type, and if the illuminance type of the current environment is the normal illuminance type, it is determined that the illumination intensity of the current environment is in the normal state.
Specifically, in this step, channel gray values corresponding to the ROI region in the red channel, the green channel, and the blue channel are respectively calculated according to the three-channel color histogram, and the channel gray values are subjected to summation calculation, average calculation, or median calculation to obtain the image gray value, which is used to represent the overall image gray level of the ROI region.
Step S30, if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
the preset white balance condition is used for determining whether white balance processing is required for the Bayer image, and the preset white balance condition may be set as a determination condition according to a requirement, for example, the determination condition may be: and judging whether the image gray value is larger than a preset gray value or whether the image gray value is within a preset gray range, and the like.
Specifically, in this step, the brightness information includes a brightness difference between an average brightness value of the image in the ROI area and a preset brightness value, and the brightness difference is used to calculate the illumination adjustment values for the red channel, the green channel, and the blue channel, respectively.
Step S40, adjusting the illumination intensity of a light supplement lamp to the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state;
wherein, this light filling lamp includes red light filling lamp, green light filling lamp and blue light filling lamp, and this illumination adjustment value is used for controlling red light filling lamp, green light filling lamp and blue light filling lamp and carries out red light compensation, green glow compensation and blue light compensation to red channel, green channel and the blue channel correspondence in the Bayer image to make the Bayer image after the light compensation be in white balance state, and then improved the image display effect of Bayer image.
In this embodiment, through adopting the light filling lamp to carry out the mode of light filling to the Bayer image, make the Bayer image be in white balance state, and because do not relate to the zoom of lens aperture, so do not cause the reduction of degree of depth of field, the image display scope of Bayer image has been ensured, through the design of the illumination intensity type according to the current environment of locating of histogram information determination, with judge whether need to adopt the light filling lamp to carry out the light filling to the Bayer image, through the design that calculates the illumination adjustment value according to the luminance information in ROI area, with the adjustment value of calculating the light filling lamp to the illumination intensity of Bayer image, and then improved the white balance effect of Bayer image.
Example two
Referring to fig. 2, a flowchart of an image white balance method according to a second embodiment of the present application is shown, where compared to the first embodiment, the image white balance method according to the present embodiment further includes steps S50 to S100 after step S10. The details are as follows:
step S50, if the illuminance type of the current environment is a normal illuminance type, starting a white balance mode in the camera, and acquiring a captured image of the camera in the white balance mode;
the white balance mode is used for performing fixed light compensation on a shot image of the camera, and comprises a red light fixed compensation value, a green light fixed compensation value and a blue light fixed compensation value which are pre-stored by a user.
Step S60, performing light color cast detection on the current environment according to the shot image;
the light color cast detection design is carried out on the current environment according to the shot image so as to judge whether the light of the current environment has color cast.
Specifically, in this step, the performing, according to the captured image, color cast detection on the illumination color of the current environment includes:
step S601, obtaining a red light gray level histogram, a green light gray level histogram and a blue light gray level histogram corresponding to the ROI area in the shot image;
step S602, calculating a standard deviation of a gray mean value according to the red light gray histogram, the green light gray histogram and the blue light gray histogram;
in this step, the calculation formula for calculating the standard deviation of the mean value of the gray scale is as follows:
wherein D isaveStandard deviation of mean value of gray scale, AvecrThe image gray value, Ave, corresponding to the red light channel in the ROI areacgThe image gray value, Ave, corresponding to the green light channel in the ROI areacbAn image gray value, Ave, corresponding to the blue light channel in the ROI areargbThe average value of the image gray values corresponding to three channels in the ROI area is obtained.
Step S603, if the standard deviation of the gray average value is not within the range of the preset standard deviation, judging that the light of the current environment has color cast;
wherein the predetermined standard deviation range is [ Targetave-biasave，Targetave+biasave]，TargetaveIs the target value of three-channel average brightness standard deviation, biasaveA first preset deviation value;
if Targetave-biasave≤Dave≤Targetave+biasaveIf the standard deviation of the gray level mean value is not within the preset standard deviation range, the light of the current environment is judged to have color cast.
Step S70, if the light of the current environment has color cast, closing the white balance mode, and determining that the illumination type of the current environment is the low illumination type;
step S80, acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
specifically, the calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI region includes:
respectively calculating image gray values corresponding to a red light channel, a green light channel and a blue light channel in the ROI according to the three-channel color histogram of the ROI and the following formula:
wherein, AvecrThe image gray value, Ave, corresponding to the red light channel in the ROI areacgThe image gray value, Ave, corresponding to the green light channel in the ROI areacbThe image gray value H corresponding to the blue light channel in the ROI areacrA color histogram, H, corresponding to a red light channel in the ROI areacgA color histogram, H, corresponding to the green channel in the ROI areacbAnd obtaining a color histogram corresponding to the blue light channel in the ROI area.
In this embodiment, if the image gray values corresponding to the red light channel, the green light channel and the blue light channel in the ROI area are all within a preset brightness value range, it is determined that the image gray values satisfy the preset white balance condition, the Bayer pattern is in a white balance state, and light compensation of a fill light is not required for the Bayer pattern;
and if the image gray value corresponding to any one of the red light channel, the green light channel and the blue light channel in the ROI region is not in the preset brightness value range, judging that the image gray value does not meet the preset white balance condition, and the Bayer image is not in a white balance state, so that light compensation of a light supplement lamp is required for the Bayer image.
Optionally, in this step, the preset brightness value range is [ Target ]lum-biaslum，Targetlum+biaslum]，TargetlumIs the target value of the average brightness of the surface of the calibration object, biaslumIs the second predetermined offset value.
Step S90, if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
wherein, the calculation formula for calculating the illumination adjustment value according to the brightness information of the ROI area is as follows:
wherein u isr(m) is an illumination adjustment value of a red light supplement lamp aiming at the mth frame image in the Bayer image, ug(m) is an illumination adjustment value of a green fill light for the mth frame image in the Bayer image, ub(m) is an illumination adjustment value of a blue light supplementary lamp for the mth frame image in the Bayer image, Kp、KiAnd KdRespectively being proportional term, differential term and integral term coefficient in PID algorithm, e (m) being brightness difference value between average brightness value and preset brightness value of mth frame image in Bayer image.
And S100, adjusting the illumination intensity of a light supplement lamp to the Bayer image according to the illumination adjustment value, so that the Bayer image is in a white balance state.
In this embodiment, whether the light of the current environment has color cast is determined by designing light color cast detection according to the shot image, and when it is determined that the light of the current environment has color cast, it is determined that the illumination type of the current environment is a low illumination type, whether the Bayer image is in a white balance state is determined by respectively determining whether the gray values of the images corresponding to the red light channel, the green light channel, and the blue light channel in the ROI region are all within a preset brightness value range, and if the gray value of the image corresponding to any one of the red light channel, the green light channel, and the blue light channel in the ROI region is not within the preset brightness value range, it is determined that the Bayer image is not in the white balance state, and light compensation of a fill light is required for the Bayer image.
EXAMPLE III
Referring to fig. 3, it is a flowchart of an image white balance method provided in a third embodiment of the present application, where the third embodiment is used to refine step S10 in the first embodiment to refine the step of describing how to determine the illumination type of the current environment according to the histogram information, and includes the steps of:
step S11, calculating a histogram average value according to the three-channel color histogram and the gray level histogram of the Bayer image, and calculating a histogram standard deviation according to the histogram average value and the gray level histogram;
the calculation formula for calculating the histogram average value according to the three-channel color histogram and the gray level histogram of the Bayer image is as follows:
in the above formula, AveHIs the mean value of the histogram, HnA gray level histogram of the mth frame in the Bayer image;
optionally, a calculation formula adopted for calculating the histogram standard deviation according to the histogram average value and the gray level histogram is as follows:
in the above formula, DHIs the histogram standard deviation.
Step S12, if the histogram average is smaller than the brightness threshold and the histogram standard deviation is smaller than the variance threshold, determining that the illumination type of the current environment is the low illumination type;
the brightness threshold and the variance threshold can be set according to the requirement.
Step S13, if the histogram average is greater than or equal to the brightness threshold, determining that the illumination type of the current environment is the normal illumination type;
step S14, if the histogram standard deviation is greater than or equal to the variance threshold, determining that the illumination type of the current environment is the normal illumination type;
in this embodiment, the illumination type of the current environment is determined by determining based on the size between the histogram average and the brightness threshold and between the histogram standard deviation and the variance threshold, and if the histogram average is smaller than the brightness threshold and the histogram standard deviation is smaller than the variance threshold, the illumination type of the current environment is determined to be the low illumination type.
Example four
Referring to fig. 4, which is a flowchart of an image white balance method according to a fourth embodiment of the present application, the fourth embodiment is different from the first embodiment in that the image white balance method further includes step S110 to step S140 after step S40. The details are as follows:
step S110, obtaining an ROI (region of interest) area of any appointed frame image in the Bayer image, and calculating a channel brightness mean value of the ROI area of the appointed frame image to obtain a current frame channel brightness mean value;
wherein, the current frame channel brightness mean values respectively comprise a red channel brightness mean value Ave of a red channel in the current framejrGreen channel brightness mean Ave of green channeljgAnd the average value Ave of the blue channel luminance of the blue channeljbSpecifically, the luminance average values of the luminance of the image corresponding to the red channel, the green channel and the blue channel in the ROI region of the specified frame image are respectively calculated to obtain the luminance average value of the current frame channel.
Step S120, obtaining an ROI (region of interest) area of a previous frame image of the appointed frame image, and calculating a channel brightness mean value of the ROI area of the previous frame image to obtain a channel brightness mean value of the previous frame;
wherein the last frame channel brightness mean values respectively comprise the red channel brightness mean values Ave 'of the red channels in the last frame'jrGreen channel luminance mean Ave 'of green channel'jgAnd blue channel luminance mean Ave 'of blue channel'jbSpecifically, the luminance average of the luminance of the image corresponding to the red channel, the green channel, and the blue channel in the ROI region of the previous frame image is calculated, so as to obtain the luminance average of the channel of the previous frame.
Specifically, in this embodiment, a calculation formula adopted to calculate the average value of the channel luminance of the ROI areas of the specified frame image and the previous frame image is as follows:
wherein a and b are the pixel size of the calibration object, AvejIs the current frame channel luminance mean, Ave'jMean value of luminance of channel of previous frame, HcIs the luminance value, H ', of the gray histogram in the designated frame image'cThe luminance value of the gray histogram in the previous frame image.
Step S130, calculating a brightness mean value difference value between the brightness mean value of the current frame channel and the brightness mean value of the previous frame channel, and calculating a steady-state parameter in the pool type function according to the brightness mean value difference value;
wherein the brightness mean difference comprises a red channel mean difference D between the current frame channel brightness mean and the previous frame channel brightness meanave_rGreen channel mean difference Dave_gAnd blue channel mean difference Dave_bAnd according to the red channel mean difference Dave_rGreen channel mean difference Dave_gAnd blue channel mean difference Dave_bThe steady state parameters in the pool type function are calculated.
Specifically, in this step, a calculation formula used for calculating the steady-state parameter in the pool type function according to the luminance mean difference value is as follows:
Jud_flag＝(Jud_flagr+Jud_flagg+Jud_flagb＝＝0)？0:1
among them, biasjr、biasjgAnd biasjbRespectively, the deviation range, bias of the preset channel brightness mean value differencejrIs the deviation range of the mean difference of the red channel, biasjgIs the deviation range of the mean difference of the green channel, biasjbThe deviation range of the blue channel mean difference value is shown. If Jud _ flag is equal to 0, it is determined that the current environment is not changed, the white balance performance of the Bayer image is stable, and the illumination intensity of the Bayer image by the red, green and blue fill light lamps does not need to be changed;
if Jud _ flag is equal to 0, it is determined that the current environment is not changed, the white balance performance of the Bayer image is stable, and the illumination intensity of the Bayer image by the red, green and blue fill light lamps does not need to be changed.
"? : "is a triage operator in the C language,"? "preceding table logic condition,"? "and": "middle" represents a value when the condition is satisfied, ": "value when the latter table condition is not satisfied. For example, when a > b, x ═ 1 or else x ═ 0, can be written as x ═ a > b1: 0.
Specifically, in this step, D is calculated in three channels of red, green and blueave_r、Dave_g、Dave_bWith the set biasjr、biasjg、biasjbCarrying out comparison;
determining the pool type value Jud _ flagr、Jud_flagg、Jud_flagb0 or 1, and further determines Jud _ flag ═ Jud _ flag (Jud _ flag)r+Jud_flagg+Jud_flagb＝＝0)？0:1。
Step S140, if the steady state parameter is a preset parameter value, determining that the white balance performance of the Bayer image is stable;
if Jud _ flag is equal to 1, it is determined that the current environment is changed, the white balance performance of the Bayer image is unstable, and the illumination intensity of the Bayer image by the red, green and blue fill-in lamps needs to be changed, and then the step S10 is returned again to perform the step of determining the current environment illumination type.
In this embodiment, the brightness average difference between the brightness average of the current frame channel and the brightness average of the previous frame channel is calculated, so that the brightness of each channel of the current frame is compared with the brightness of each channel of the previous frame, and the design of the steady-state parameter in the pool type function is calculated according to the brightness average difference, so as to determine whether the steady state of the current environment changes, and when it is determined that the steady state of the current environment changes, the illumination intensity of the light supplement lamp on the Bayer image is readjusted.
EXAMPLE five
Fig. 5 shows a schematic structural diagram of an image white balance system 100 provided in a fifth embodiment of the present application, corresponding to the image white balance method described in the above embodiments, and only shows parts related to the embodiments of the present application for convenience of explanation.
Referring to fig. 5, the system includes: illuminance type determination module 10, gray value calculation module 11, adjustment value calculation module 12, and fill light adjustment module 13, wherein:
the illumination type determining module 10 is configured to acquire histogram information of a Bayer image of a camera in a current environment, and determine an illumination type of the current environment according to the histogram information, where the illumination type is used to represent illumination intensity of the current environment.
Wherein the illumination type determination module 10 is further configured to: if the illumination type of the current environment is a normal illumination type, starting a white balance mode in the camera, and acquiring the shot image in the white balance mode;
carrying out light color cast detection on the current environment according to the shot image;
if the light of the current environment has color cast, closing the white balance mode, and judging that the illumination type of the current environment is the low illumination type;
acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
and adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
Preferably, the illuminance type determination module 10 is further configured to: calculating a histogram average value according to a three-channel color histogram and a gray level histogram of the Bayer image, and calculating a histogram standard deviation according to the histogram average value and the gray level histogram;
if the mean value of the histogram is smaller than a brightness threshold value and the standard deviation of the histogram is smaller than a variance threshold value, judging that the illumination type of the current environment is the low illumination type;
if the histogram average value is larger than or equal to a brightness threshold value, judging that the illumination type of the current environment is the normal illumination type;
and if the standard deviation of the histogram is larger than or equal to a variance threshold value, judging that the illumination type of the current environment is the normal illumination type.
Furthermore, the illuminance type determination module 10 is further configured to: acquiring a red light gray level histogram, a green light gray level histogram and a blue light gray level histogram corresponding to the ROI area in the shot image;
calculating a standard deviation of a gray mean value according to the red light gray histogram, the green light gray histogram and the blue light gray histogram;
and if the standard deviation of the gray average value is not within the preset standard deviation range, judging that the light of the current environment has color cast.
And the gray value calculation module 11 is configured to obtain a three-channel color histogram of an ROI in the Bayer image if the illumination type of the current environment is a low illumination type, and calculate an image gray value of the Bayer image according to the three-channel color histogram of the ROI.
Wherein, the gray calculating module 11 is further configured to: respectively calculating image gray values corresponding to a red light channel, a green light channel and a blue light channel in the ROI according to the three-channel color histogram of the ROI and the following formula:
wherein, AvecrThe image gray value, Ave, corresponding to the red light channel in the ROI areacgThe image gray value, Ave, corresponding to the green light channel in the ROI areacbIn the ROI regionImage gray scale value, H, corresponding to blue light channelcrA color histogram, H, corresponding to a red light channel in the ROI areacgA color histogram, H, corresponding to the green channel in the ROI areacbAnd obtaining a color histogram corresponding to the blue light channel in the ROI area.
In addition, the gray value calculating module 11 is further configured to: if the gray values of the images corresponding to the red light channel, the green light channel and the blue light channel in the ROI area are all within a preset brightness value range, judging that the gray values of the images meet the preset white balance condition;
and if the image gray value corresponding to any one of the red light channel, the green light channel and the blue light channel in the ROI area is not in the preset brightness value range, judging that the image gray value does not meet the preset white balance condition.
A regulating value calculating module 12, configured to calculate an illumination regulating value according to the brightness information of the ROI area if the gray value of the image does not satisfy a preset white balance condition, where a calculation formula adopted for calculating the illumination regulating value according to the brightness information of the ROI area is:
wherein u isr(m) is an illumination adjustment value of a red light supplement lamp aiming at the mth frame image in the Bayer image, ug(m) is an illumination adjustment value of a green fill light for the mth frame image in the Bayer image, ub(m) is an illumination adjustment value of a blue light supplementary lamp for the mth frame image in the Bayer image, Kp、KiAnd KdRespectively being proportional term, differential term and integral term coefficient in PID algorithm, e (m) being brightness difference value between average brightness value and preset brightness value of mth frame image in Bayer image.
And a light supplement lamp adjusting module 13, configured to adjust the light supplement lamp according to the illumination adjustment value to the illumination intensity of the Bayer image, so that the Bayer image is in a white balance state.
Optionally, the image white balance apparatus 100 further includes:
the steady-state detection module 14 is configured to obtain an ROI region of any one of the designated frame images in the Bayer image, and calculate a channel luminance mean value of the ROI region of the designated frame image to obtain a current frame channel luminance mean value;
obtaining an ROI (region of interest) area of a previous frame image of the appointed frame image, and calculating a channel brightness mean value of the ROI area of the previous frame image to obtain a channel brightness mean value of the previous frame;
calculating a brightness mean value difference value between the brightness mean value of the current frame channel and the brightness mean value of the previous frame channel, and calculating a steady-state parameter in a pool type function according to the brightness mean value difference value;
and if the steady-state parameter is a preset parameter value, determining that the white balance performance of the Bayer image is stable.
In this embodiment, through adopting the light filling lamp to carry out the mode of light filling to the Bayer image, make the Bayer image be in white balance state, and because do not relate to the zoom of lens aperture, so do not cause the reduction of degree of depth of field, the image display scope of Bayer image has been ensured, through the design of the illumination intensity type according to the current environment of locating of histogram information determination, with judge whether need to adopt the light filling lamp to carry out the light filling to the Bayer image, through the design that calculates the illumination adjustment value according to the luminance information in ROI area, with the adjustment value of calculating the light filling lamp to the illumination intensity of Bayer image, and then improved the white balance effect of Bayer image.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/modules, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and reference may be made to the part of the embodiment of the method specifically, and details are not described here.
Fig. 6 is a schematic structural diagram of a terminal device 2 according to a sixth embodiment of the present application. As shown in fig. 6, the terminal device 2 of this embodiment includes: at least one processor 20 (only one processor is shown in fig. 6), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, the steps of any of the various method embodiments described above being implemented when the computer program 22 is executed by the processor 20.
The terminal device 2 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 20, a memory 21. Those skilled in the art will appreciate that fig. 6 is merely an example of the terminal device 2, and does not constitute a limitation of the terminal device 2, and may include more or less components than those shown, or combine some components, or different components, such as an input-output device, a network access device, and the like.
The Processor 20 may be a Central Processing Unit (CPU), and the Processor 20 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 21 may in some embodiments be an internal storage unit of the terminal device 2, such as a hard disk or a memory of the terminal device 2. The memory 21 may also be an external storage device of the terminal device 2 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the terminal device 2. The memory 21 is used for storing an operating system, an application process, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 21 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
An embodiment of the present application further provides a network device, where the network device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An image white balance method, the method comprising:
acquiring histogram information of a Bayer image of a camera in a current environment, and determining the illumination type of the current environment according to the histogram information, wherein the histogram information comprises a three-channel color histogram and a gray level histogram, and the illumination type is used for representing the illumination intensity of the current environment;
if the illumination type of the current environment is a low illumination type, acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI; the ROI is obtained by setting a white calibration object in the current environment, and is a regional image of the white calibration object in the Bayer image;
if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
and adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
2. The image white balance method according to claim 1, wherein after determining the illumination type of the current environment according to the three-channel color histogram of the Bayer image, the method further comprises:
if the illumination type of the current environment is a normal illumination type, starting a white balance mode in the camera, and acquiring a shot image of the camera in the white balance mode;
carrying out light color cast detection on the current environment according to the shot image;
if the light of the current environment has color cast, closing the white balance mode, and judging that the illumination type of the current environment is the low illumination type;
acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image, and calculating an image gray value of the Bayer image according to the three-channel color histogram of the ROI;
if the image gray value does not meet the preset white balance condition, calculating an illumination adjustment value according to the brightness information of the ROI;
and adjusting the illumination intensity of a light supplement lamp on the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
3. The image white balance method of claim 2, wherein the determining the illumination type of the current environment according to the three-channel color histogram of the Bayer image comprises:
calculating a histogram average value according to a three-channel color histogram and a gray level histogram of the Bayer image, and calculating a histogram standard deviation according to the histogram average value and the gray level histogram;
if the mean value of the histogram is smaller than a brightness threshold value and the standard deviation of the histogram is smaller than a variance threshold value, judging that the illumination type of the current environment is the low illumination type;
if the histogram average value is larger than or equal to a brightness threshold value, judging that the illumination type of the current environment is the normal illumination type;
and if the standard deviation of the histogram is larger than or equal to a variance threshold value, judging that the illumination type of the current environment is the normal illumination type.
4. The image white balance method of claim 1 or 2, wherein the calculating an image gray value of the Bayer image from a three-channel color histogram of the ROI region comprises:
respectively calculating image gray values corresponding to a red light channel, a green light channel and a blue light channel in the ROI according to the three-channel color histogram of the ROI and the following formula:
wherein, AvecrThe image gray value, Ave, corresponding to the red light channel in the ROI areacgThe image gray value, Ave, corresponding to the green light channel in the ROI areacbThe image gray value H corresponding to the blue light channel in the ROI areacrA color histogram, H, corresponding to a red light channel in the ROI areacgA color histogram, H, corresponding to the green channel in the ROI areacbAnd a and b are color histograms corresponding to blue light channels in the ROI area, and the pixel size of the calibration object is defined as a and b.
5. The image white balance method of claim 4, wherein after the separately calculating the image gray values corresponding to the red channel, the green channel and the blue channel in the ROI area, further comprises:
if the gray values of the images corresponding to the red light channel, the green light channel and the blue light channel in the ROI area are all within a preset brightness value range, judging that the gray values of the images meet the preset white balance condition;
and if the image gray value corresponding to any one of the red light channel, the green light channel and the blue light channel in the ROI area is not in the preset brightness value range, judging that the image gray value does not meet the preset white balance condition.
6. The image white balance method according to claim 1, wherein the illumination adjustment value is calculated based on the luminance information of the ROI region using a calculation formula of:
wherein u isr(m) is an illumination adjustment value of a red light supplement lamp aiming at the mth frame image in the Bayer image, ug(m) is an illumination adjustment value of a green fill light for the mth frame image in the Bayer image, ub(m) is an illumination adjustment value of a blue light supplementary lamp for the mth frame image in the Bayer image, Kp、KiAnd KdRespectively being proportional term, differential term and integral term coefficient in PID algorithm, e (m) being brightness difference value between average brightness value and preset brightness value of mth frame image in Bayer image.
7. The image white balance method of claim 1, wherein the adjusting the illumination intensity of the Bayer image by a fill light according to the illumination adjustment value so that the Bayer image is in a white balance state further comprises:
obtaining an ROI (region of interest) area of any appointed frame image in the Bayer image, and calculating a channel brightness mean value of the ROI area of the appointed frame image to obtain a current frame channel brightness mean value;
obtaining an ROI (region of interest) area of a previous frame image of the appointed frame image, and calculating a channel brightness mean value of the ROI area of the previous frame image to obtain a channel brightness mean value of the previous frame;
calculating a brightness mean value difference value between the brightness mean value of the current frame channel and the brightness mean value of the previous frame channel, and calculating a steady-state parameter in a pool type function according to the brightness mean value difference value;
and if the steady-state parameter is a preset parameter value, determining that the white balance performance of the image of the Bayer image is stable.
8. An image white balance system, comprising:
the illumination type determining module is used for acquiring histogram information of a Bayer image of a camera in a current environment, and determining an illumination type of the current environment according to the histogram information, wherein the histogram information comprises a three-channel color histogram and a gray level histogram, and the illumination type is used for representing the illumination intensity of the current environment;
the gray value calculation module is used for acquiring a three-channel color histogram of an ROI (region of interest) in the Bayer image if the illumination type of the current environment is a low illumination type, and calculating the image gray value of the Bayer image according to the three-channel color histogram of the ROI; the ROI is obtained by setting a white calibration object in the current environment, and is a regional image of the white calibration object in the Bayer image;
the adjusting value calculating module is used for calculating an illumination adjusting value according to the brightness information of the ROI if the gray value of the image does not meet a preset white balance condition;
and the light supplement lamp adjusting module is used for adjusting the light supplement lamp to the illumination intensity of the Bayer image according to the illumination adjusting value, so that the Bayer image is in a white balance state.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5619347A (en) * | 1994-09-28 | 1997-04-08 | Matsushita Electric Industrial Co., Ltd. | Apparatus for calculating a degree of white balance adjustment for a picture |
| JP2005175598A (en) * | 2003-12-08 | 2005-06-30 | Nikon Corp | Electronic camera with white balance regulating function and program |
| CN101916431A (en) * | 2010-07-23 | 2010-12-15 | 北京工业大学 | Low-illumination image data processing method and system |
| CN103093429A (en) * | 2013-01-18 | 2013-05-08 | 金三立视频科技（深圳）有限公司 | Image intensification method |
| CN108174172A (en) * | 2017-12-25 | 2018-06-15 | 广东欧珀移动通信有限公司 | Image pickup method and device, computer readable storage medium and computer equipment |
| CN111034170A (en) * | 2017-08-16 | 2020-04-17 | 高通股份有限公司 | Image capturing apparatus with stable exposure or white balance |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6493468B1 (en) * | 1998-05-06 | 2002-12-10 | Canon Kabushiki Kaisha | Image processing apparatus and method |
| TWI281084B (en) * | 2005-01-27 | 2007-05-11 | Arima Computer Corp | White balance method |
| JP5178170B2 (en) * | 2007-12-11 | 2013-04-10 | オリンパス株式会社 | White balance adjusting device and white balance adjusting method |
| US20120001841A1 (en) * | 2010-06-30 | 2012-01-05 | Jeff Gokingco | Identifying ambient light type and illuminance compensation using a plurality of photodetectors |
| KR20120068655A (en) * | 2010-12-19 | 2012-06-27 | 김인선 | Method and camera device for capturing iris or subject of good quality with one bandpass filter passing both visible ray and near infra red ray |
| CN102348048A (en) * | 2011-09-16 | 2012-02-08 | 中山大学 | Self-adaptive time-space domain cumulative filtering and tone mapping video enhancement method |
| JP6000133B2 (en) * | 2013-01-08 | 2016-09-28 | 株式会社 日立産業制御ソリューションズ | Imaging apparatus, imaging system, and imaging method |
| KR102446442B1 (en) * | 2015-11-24 | 2022-09-23 | 삼성전자주식회사 | Digital photographing apparatus and the operating method for the same |
| CN111372007B (en) * | 2020-03-03 | 2021-11-12 | 荣耀终端有限公司 | Ambient light illumination detection method and device and electronic equipment |
-
2020
- 2020-09-14 CN CN202010960221.9A patent/CN112218065B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5619347A (en) * | 1994-09-28 | 1997-04-08 | Matsushita Electric Industrial Co., Ltd. | Apparatus for calculating a degree of white balance adjustment for a picture |
| JP2005175598A (en) * | 2003-12-08 | 2005-06-30 | Nikon Corp | Electronic camera with white balance regulating function and program |
| CN101916431A (en) * | 2010-07-23 | 2010-12-15 | 北京工业大学 | Low-illumination image data processing method and system |
| CN103093429A (en) * | 2013-01-18 | 2013-05-08 | 金三立视频科技（深圳）有限公司 | Image intensification method |
| CN111034170A (en) * | 2017-08-16 | 2020-04-17 | 高通股份有限公司 | Image capturing apparatus with stable exposure or white balance |
| CN108174172A (en) * | 2017-12-25 | 2018-06-15 | 广东欧珀移动通信有限公司 | Image pickup method and device, computer readable storage medium and computer equipment |
Non-Patent Citations (1)
| Title |
|---|
| 基于R_G_B_G直方图自动白平衡算法的研究与实现;李锐锋;《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》;20150227;全文 * |
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