WO2021051382A1 - Procédé et dispositif de traitement de balance des blancs, plate-forme mobile et caméra - Google Patents

Procédé et dispositif de traitement de balance des blancs, plate-forme mobile et caméra Download PDF

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Publication number
WO2021051382A1
WO2021051382A1 PCT/CN2019/106950 CN2019106950W WO2021051382A1 WO 2021051382 A1 WO2021051382 A1 WO 2021051382A1 CN 2019106950 W CN2019106950 W CN 2019106950W WO 2021051382 A1 WO2021051382 A1 WO 2021051382A1
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Prior art keywords
image
value
gray
white balance
pixel
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PCT/CN2019/106950
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English (en)
Chinese (zh)
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肖瑾
曹子晟
胡攀
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深圳市大疆创新科技有限公司
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Priority to CN201980033268.6A priority Critical patent/CN112204957A/zh
Priority to PCT/CN2019/106950 priority patent/WO2021051382A1/fr
Publication of WO2021051382A1 publication Critical patent/WO2021051382A1/fr

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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

Definitions

  • the present disclosure relates to the field of image processing technology, in particular, to white balance processing methods and equipment, movable platforms, and cameras.
  • the human visual system has the characteristic of color constancy, that is, the human eye can adapt to different lighting and restore the color of the scene under different lighting to the color of the scene illuminated by white light.
  • the image acquisition device for example, a camera
  • Traditional white balance processing methods can be roughly divided into two categories: based on statistical priors and based on learning algorithms.
  • the first method is mostly based on making observations on images with no color cast and assuming a priori, and then use the statistical information of the image to infer the illumination of the image. But this kind of algorithm basically assumes that the normal image conforms to a certain prior knowledge. In some extreme cases, the prior knowledge may not be satisfied. In this way, algorithms based on statistical priors will produce large errors.
  • the embodiments of the present disclosure propose a white balance processing method and device, a movable platform, and a camera to solve the technical problem of low accuracy in related technologies.
  • a white balance processing method includes:
  • a white balance processing device includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor executes The program implements the following methods:
  • a movable platform including:
  • a power system installed in the machine body and used to provide power to the movable platform;
  • the white balance processing device described in any embodiment.
  • a camera including:
  • the lens assembly is arranged inside the housing;
  • a sensor assembly which is arranged inside the housing, and is used to sense light passing through the lens assembly and generate an electrical signal
  • the white balance processing device described in any embodiment.
  • a computer-readable storage medium is provided, and a number of computer instructions are stored on the readable storage medium, and the computer instructions implement the steps of the method described in any embodiment when the computer instructions are executed.
  • Fig. 1 is a flowchart of a white balance processing method according to an embodiment of the present disclosure.
  • Fig. 2 is an effect comparison diagram of a histogram constructed according to the prior art and the present invention according to an embodiment of the present disclosure.
  • Fig. 3 shows a network framework of a gray detection network according to an embodiment of the present disclosure.
  • Fig. 4 is a schematic structural diagram of a computer device for implementing the method of the embodiment of the present disclosure according to the embodiment of the present disclosure.
  • Fig. 5 is a block diagram showing a movable platform according to an embodiment of the present disclosure.
  • Fig. 6 is a block diagram showing a camera according to an embodiment of the present disclosure.
  • first, second, third, etc. may be used in this specification to describe various information, the information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • first information may also be referred to as second information, and similarly, the second information may also be referred to as first information.
  • word “if” as used herein can be interpreted as "when” or “when” or "in response to determination”.
  • FIG. 1 it is a flowchart of a white balance processing method according to an embodiment of the present disclosure.
  • the method may include:
  • Step S101 detecting gray points on the image to be processed
  • Step S102 Construct a histogram according to the gray points
  • Step S103 Estimate the illumination of the image to be processed according to the histogram, and perform white balance processing on the image to be processed according to the illumination.
  • the pixel when a certain pixel on an image satisfies: the R channel color component, G channel color component, and B channel color component of the pixel on the image are equal, then the pixel is the Gray dots on the image.
  • an embodiment of the present specification uses a machine learning model to detect gray points.
  • the image to be processed can be input into a pre-trained machine learning model to obtain the first probability value of each pixel on the image to be processed as a gray point; the pixel with the first probability value greater than the preset value is taken as Gray point.
  • the first probability value can be set based on experience. The larger the first probability value, the higher the confidence that the corresponding pixel point is a gray point. Detecting gray points through a machine learning model improves the accuracy of gray point detection, thereby improving the white balance processing effect.
  • the machine learning model used to detect gray points is a convolutional neural network model (Convolutional Neural Networks, CNN).
  • CNN convolutional Neural Networks
  • Other neural network models can also be used here, or other machine learning models can be used. Since CNN has a relatively strong expressive ability and can effectively extract key features, the use of CNN to detect gray points can further improve the detection accuracy.
  • the above-mentioned convolutional neural network model can be obtained by training according to the training image and the training label of each pixel on the training image, and according to the preset loss function.
  • the light of the image is given instead of the label of the gray point. Labeling gray dots is not only time-consuming, but also expensive. Because the definition of gray under white light is clear, that is, the gray point satisfies:
  • L c represents the color value of the light L on the R, G, and B color channels.
  • the training label of each pixel on the training image is determined according to the second probability value, namely:
  • p(h,w) represents the second probability value corresponding to the pixel at the coordinates (h,w) on the training image
  • ⁇ ,> represents the inner product operation
  • I c (h,w) represents the The RGB value of the pixel at the coordinates (h, w) on the training image
  • the full image of the training image can be used as input, and the CNN can be trained according to the loss function.
  • the embodiment of this specification adopts the Unet neural network structure to maintain the spatial size of the output.
  • the input of the Unet neural network is a color image of H*W*3, and the output is the third probability value of H*W, denoted as Among them, H and W are the pixel height and pixel width of the training image, respectively.
  • the loss function is a cross entropy function.
  • the loss function of an embodiment can be obtained according to the following formula, namely:
  • Loss is the loss function
  • the third probability value corresponding to the pixel at the coordinates (h, w) on the training image output by the convolutional neural network model, p(h, w) indicates that the coordinates on the training image are (h, The second probability value corresponding to the pixel at w).
  • the test image can be input into the network to obtain the predicted second probability value, and then set the threshold ⁇ , if the pixels in the test image The predicted second probability value of the point is greater than the threshold ⁇ , then it is determined that the pixel point is a gray point; otherwise, it is determined that the pixel point is a color point. According to the accuracy of the predicted second probability value, the performance of the trained machine learning model can be verified. If the accuracy is greater than the preset accuracy threshold, the gray points on the image to be processed can be detected according to the trained model.
  • I and W represent the observed image and the image illuminated by natural white light (ie ideal image)
  • L represents the illumination
  • c represents the three color channels of R, G, and B
  • n is the length and width subscript of the image (image) .
  • the color values in the RGB space may be mapped to a preset target space to obtain the mapping value of the RGB value in the target space, and then a histogram is constructed according to the mapping value.
  • the multiplicative relationship between the image and the illumination is converted into an additive relationship.
  • the step of mapping the RGB value of the gray point on the image to be processed to a preset target space, and obtaining the mapping value of the RGB value on the target space includes: according to the gray point
  • the first mapping value of the target space is generated from the R channel color component and the G channel color component of the gray point
  • the second mapping value of the target space is generated according to the G channel color component and the B channel color component of the gray point. Assuming that the first mapping value and the second mapping value are represented by u and v, respectively, then:
  • r, g, and b are the R channel color component, G channel color component, and B channel color component of the gray point on the image to be processed, respectively.
  • I u , Wu and Lu are the observation image I, the first mapping value of the ideal image W and the illumination L in the target space
  • I v , W v and L v are the observation image I, the ideal image W and the illumination respectively The second mapping value of L in the target space.
  • the image W and the light L conform to the multiplicative relationship in the RGB space. Due to the nature of the log function, after turning to the uv space, the multiplicative relationship is converted into an additive relationship. Calculate the two-dimensional statistical histogram for the image ⁇ W u ,W v ⁇ in uv space. Due to the additive relationship, the deviation of the histogram pattern corresponds to ⁇ L u ,L v ⁇ , and then convert it back to RGB space. Get light ⁇ L r ,L g ,L b ⁇ .
  • each dimension of the target space can be evenly divided into several equal parts to obtain multiple intervals; according to the mapping value, the number of gray points falling in each interval can be counted; The number of gray points corresponding to each interval constructs a histogram.
  • the u-axis and v-axis of the uv space can be divided into N equal parts to obtain N*N intervals (N is a positive integer), where the gray point in the i-th space satisfies: U i1 ⁇ u i ⁇ U i2, V i1 ⁇ v i ⁇ V i2, 1 ⁇ i ⁇ N, u i and V i represent the first mapping and the second mapping value within the i th interval value gray points, U i1 and U i2 respectively Represents the lower limit and upper limit of the first mapping value of the i-th interval, and Vi1 and Vi2 represent the lower limit and upper limit of the second mapping value of the i-th interval, respectively.
  • the first mapping value of a gray point falls within the value range of the first mapping value in the corresponding interval
  • the second mapping value of the gray point falls within the value range of the second mapping value in the corresponding interval
  • the The gray points are the gray points in the interval. Therefore, the number of pixel points in each interval can be calculated according to the first mapping value and the second mapping value of each gray point and the upper and lower limits of the first mapping value of each interval.
  • FIG. 2 shows the effect comparison diagram of the histogram constructed in the prior art and the present invention, in which the left side is the histogram constructed in the prior art, and the right figure is the histogram constructed in the embodiment of the present invention. It can be seen that, compared with the histogram of the prior art, the histogram based on gray points proposed by the present invention removes the discrete noise pattern in the histogram, thereby making the subsequent illumination estimation simpler and more accurate.
  • the embodiment of this specification may use a preselected filter template and the histogram to do sliding window convolution to obtain the filtered response, and then use the illumination of the pixel with the largest response as the waiting Process the lighting of the image.
  • H I represents the histogram
  • F represents the filter template
  • the illumination ⁇ L r ,L g ,L b ⁇ can be obtained.
  • white balance processing can be performed according to the illumination ⁇ L r , L g , L b ⁇ .
  • it can also be based on with Perform white balance processing to obtain W u and W v corresponding to the uv space respectively, and then inversely map W u and W v to the RGB space to generate an ideal image.
  • the traditional white balance processing method generally uses each pixel in the image to be processed for white balance processing. However, some color points that deviate greatly from gray may have no effect on the final estimated illumination, or even have a negative effect. . Using all pixels, including color points, for white balance processing may cause the histogram to be too scattered, including large deviated noise points, which will affect the final lighting calculation, resulting in poor white balance processing.
  • the network framework of a gray detection network shown in the embodiment of the present disclosure is shown in FIG. 3.
  • the image to be processed is input into a machine learning model (for example, CNN) to detect gray points in the image to be processed, and then the RGB values of the gray points are spatially converted to obtain the mapping value corresponding to the RGB value, according to the mapping value
  • a machine learning model for example, CNN
  • the RGB values of the gray points are spatially converted to obtain the mapping value corresponding to the RGB value, according to the mapping value
  • To construct a histogram, and estimate the illumination based on the histogram because only the gray points useful for illumination estimation are used when constructing the histogram, and the color points that may interfere with the illumination estimation are removed, so the accuracy of the illumination estimation is improved;
  • the generalization ability is improved.
  • the gray points only occupy a small part of the pixels on the image to be processed, the amount of data is reduced, the computational complexity is reduced, and the white balance processing efficiency is improved.
  • the embodiment of this specification achieves the numerical effect of ranking first.
  • the white balance method based on gray detection proposed in the embodiments of this specification shows better cross-database generalization ability than other learning algorithms.
  • the white balance processing device in the embodiment of this specification may be, for example, a server or a terminal device.
  • the method embodiments can be implemented by software, or can be implemented by hardware or a combination of software and hardware. Taking software implementation as an example, as a logical device, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory by the processor that processes the file where it is located. From a hardware perspective, as shown in FIG. 4, it is a hardware structure diagram of the white balance processing device 400 that implements the method of this specification, except for the processor 401, the memory 402, the network interface 403, and the hardware structure shown in FIG. In addition to the lossy memory 404, the white balance processing device used to implement the method of the present specification in the embodiment may also include other hardware according to the actual function of the white balance processing device, which will not be repeated here.
  • the embodiment of this specification provides a white balance processing device, the white balance processing device includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor executes the following when the program is executed method:
  • the traditional white balance processing method generally uses each pixel in the image to be processed for white balance processing. However, some color points that deviate greatly from gray may have no effect on the final estimated illumination, or even have a negative effect. . Using all pixels, including color points, for white balance processing may cause the histogram to be too scattered, including large deviated noise points, which will affect the final lighting calculation, resulting in poor white balance processing.
  • the gray points useful for illumination estimation are used when constructing the histogram, and the color points that may interfere with the illumination estimation are removed. Therefore, the accuracy of the illumination estimation is improved; at the same time, because different image acquisition devices collect The gray of the image has commonality, so the generalization ability is improved.
  • the gray points only occupy a small part of the pixels on the image to be processed, the amount of data is reduced, the computational complexity is reduced, and the white balance processing efficiency is improved.
  • the step of the processor constructing a histogram according to the gray point includes: mapping the RGB value of the gray point on the image to be processed to a preset target space to obtain the RGB value in the The mapping value on the target space; a histogram is constructed according to the mapping value.
  • the multiplicative relationship between the image and the illumination is converted into an additive relationship. Through space conversion, the multiplicative relationship is converted into an additive relationship, which is convenient for processing and reduces the computational complexity.
  • the RGB value includes an R channel color component, a G channel color component, and a B channel color component
  • the mapping value includes a first mapping value and a second mapping value
  • the processor places the gray point at the The RGB value on the image to be processed is mapped to a preset target space
  • the step of obtaining the mapping value of the RGB value on the target space includes: generating the RGB value based on the R channel color component and the G channel color component of the gray point.
  • the first mapping value of the target space generates the second mapping value of the target space according to the G channel color component and the B channel color component of the gray point. Assuming that the first mapping value and the second mapping value are represented by u and v, respectively, then:
  • r, g, and b are the R channel color component, G channel color component, and B channel color component of the gray point on the image to be processed, respectively.
  • the step of the processor detecting gray points on the image to be processed includes: inputting the image to be processed into a pre-trained machine learning model to obtain the first probability value of each pixel on the image to be processed as a gray point ;
  • the pixel points with the first probability value greater than the preset value are regarded as gray points.
  • the machine learning model is a convolutional neural network model. Since CNN has a relatively strong expressive ability and can effectively extract key features, the use of CNN to detect gray points can further improve the detection accuracy.
  • the processor executes the program, the following method is also implemented: training the convolutional neural network model according to the training image and the training label of each pixel on the training image, and according to a preset loss function.
  • the processor executes the program, the following method is also implemented: calculating the corresponding pixel as the first gray point according to the RGB value of each pixel on the training image and the RGB value of the gray point on the training image. Two probability values; the training label of each pixel on the training image is determined according to the second probability value.
  • the light of the image is given instead of the label of the gray point. Labeling gray dots is not only time-consuming, but also expensive. By detecting the gray points in the above manner, marking the gray points one by one is avoided, and the efficiency of gray point detection is improved.
  • the step of calculating the second probability value of the corresponding pixel as a gray point by the processor according to the RGB value of each pixel on the training image and the RGB value of the gray point on the training image respectively includes: The inner product of the RGB value of the gray point on the training image and the RGB value of each pixel on the training image; and the second probability value of the corresponding pixel point being a gray point is calculated according to the inner product.
  • the loss function is a cross entropy function.
  • the step of the processor constructing a histogram according to the mapping value includes: evenly dividing each dimension of the target space into a number of equal parts to obtain a plurality of intervals; according to the mapping value, statistics fall within The number of gray points in each interval; construct a histogram according to the number of gray points corresponding to each interval.
  • the step of the processor estimating the illumination of the image to be processed according to the histogram includes: using a filter template and histogram to perform sliding window convolution to obtain a filtered response; The illumination is used as the illumination of the image to be processed.
  • the embodiment of this specification also provides a movable platform 500, including: a body 501; a power system 502 installed in the body 501 to provide power for the movable platform; and any The white balance processing device 400 described in the embodiment.
  • the movable platform 500 is a vehicle, a drone or a movable robot.
  • an embodiment of the present specification also provides a camera 600, which includes: a housing 601; a lens assembly 602, which is arranged inside the housing 601; a sensor assembly 603, which is arranged inside the housing 601 for sensing passing The light of the lens assembly 602 generates an electrical signal; and the white balance processing device 400 according to any one of the embodiments.
  • the embodiments of this specification also provide a computer-readable storage medium on which a number of computer instructions are stored, and when the computer instructions are executed, the steps of the method described in any of the embodiments are implemented.
  • the embodiments of this specification may adopt the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program codes.
  • Computer usable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include, but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.
  • PRAM phase change memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • RAM random access memory
  • ROM read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory or other memory technology
  • CD-ROM compact disc
  • DVD digital versatile disc
  • Magnetic cassettes magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.

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Abstract

La présente invention concerne un procédé et un dispositif de traitement de balance des blancs, ainsi qu'une plate-forme mobile et une caméra. Le procédé comprend les étapes consistant à : premièrement détecter des points gris dans une image ; puis utiliser tous les points gris pour construire un histogramme statistique ; et enfin, utiliser l'histogramme pour estimer l'éclairage et effectuer un traitement de balance des blancs. Du fait que l'histogramme est construit uniquement à l'aide des points gris, qui sont utiles pour une estimation d'éclairage, et que les points de couleur, qui peuvent interférer avec l'estimation d'éclairage, sont éliminés, la précision de l'estimation d'éclairage est améliorée ; de plus, différents appareils de collecte d'image ont la collecte de la couleur grise d'une image en commun, et la généralisation est ainsi améliorée.
PCT/CN2019/106950 2019-09-20 2019-09-20 Procédé et dispositif de traitement de balance des blancs, plate-forme mobile et caméra WO2021051382A1 (fr)

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CN201980033268.6A CN112204957A (zh) 2019-09-20 2019-09-20 白平衡处理方法和设备、可移动平台、相机
PCT/CN2019/106950 WO2021051382A1 (fr) 2019-09-20 2019-09-20 Procédé et dispositif de traitement de balance des blancs, plate-forme mobile et caméra

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