CN119110168A - A multi-camera device white balance intelligent adjustment and optimization system and method - Google Patents
A multi-camera device white balance intelligent adjustment and optimization system and method Download PDFInfo
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Abstract
本发明公开一种多目摄像机设备白平衡智能调节与优化系统及其方法，包括：多目图像采集单元，用于从不同角度捕获多目摄像机设备所在环境的图像，形成多角度视图；环境光线检测模块，用于评估多目摄像机所处环境的光照条件，并提供光照强度、色温和方向的测量数据；环境光色温分析算法，用于计算当前环境的色温值；智能白平衡调整单元，根据环境光色温分析算法计算的结果自动调整多目摄像机的白平衡设置。本发明通过环境光线检测模块来实时检测多目摄像机设备的光线条件和拍摄环境，并根据这些因素智能调节多目摄像机的白平衡参数，能够大大提高多目摄像机设备的白平衡调节精度和效率，从而获得更加准确、真实的图像。
The present invention discloses a white balance intelligent adjustment and optimization system and method for a multi-camera device, comprising: a multi-camera image acquisition unit, used to capture images of the environment in which the multi-camera device is located from different angles to form a multi-angle view; an ambient light detection module, used to evaluate the lighting conditions of the environment in which the multi-camera device is located, and provide measurement data of light intensity, color temperature and direction; an ambient light color temperature analysis algorithm, used to calculate the color temperature value of the current environment; an intelligent white balance adjustment unit, which automatically adjusts the white balance setting of the multi-camera according to the result calculated by the ambient light color temperature analysis algorithm. The present invention detects the lighting conditions and shooting environment of the multi-camera device in real time through the ambient light detection module, and intelligently adjusts the white balance parameters of the multi-camera according to these factors, which can greatly improve the white balance adjustment accuracy and efficiency of the multi-camera device, thereby obtaining a more accurate and realistic image.
Description
Technical Field
The invention relates to a multi-camera, in particular to an intelligent white balance adjusting and optimizing system and method for multi-camera equipment.
Background
Currently, there are applications in many fields for simultaneously capturing images of a plurality of different cameras, such as a multi-view camera for security monitoring, a multi-view camera product for consumer electronics, etc.
In existing multi-camera devices, accurate adjustment of the white balance is critical to obtaining high quality images. However, because the multi-camera device has differences in hardware design, device type selection, shooting angle and the like, the traditional white balance adjustment method often cannot meet the requirements of the multi-camera device on the accuracy and efficiency of white balance adjustment, larger chromatic aberration of pictures acquired by different cameras easily occurs, the color effect of an actual scene cannot be accurately and objectively displayed, and more troubles are brought to users.
The defects existing in the prior art mainly comprise:
the precision is not high, the environment color temperature where the multi-camera is detected by the external sensor has precision deviation, and meanwhile, the detection and the distinction of a wide dynamic range cannot be satisfied.
The cost is high, and additional cost is required to be added for the hardware sensor to acquire the ambient color temperature hardware to be matched with the multi-camera.
The complexity of the system, namely, the scheme of detecting through an external sensor, requires a plurality of parts to be linked to generate an effect, the complexity of the system is higher, related variables influencing the performance of the system are increased, and the risk of abnormal system performance is increased.
If the environment detection and recognition are performed through the respective pictures, different color displays are displayed in the similar scene, and the judgment of the user on the actual environment scene is affected.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an intelligent white balance adjusting and optimizing system and method for a multi-camera device, the system can automatically detect the factors such as the light conditions and the shooting environment of the multi-camera equipment, and intelligently adjust the white balance parameters of the multi-path picture according to the factors so as to obtain a more real and accurate image.
In order to achieve the above object, the present invention provides an intelligent white balance adjustment and optimization system for a multi-camera device, comprising:
the multi-view image acquisition unit is used for capturing images of the environment where the multi-view camera equipment is located from different angles to form a multi-angle view;
The environment light detection module is used for evaluating the illumination conditions of the environment where the multi-camera is positioned and providing measurement data of illumination intensity, color temperature and direction;
the ambient light color temperature analysis algorithm is used for processing and analyzing the image data captured by the multi-view image acquisition unit and the measurement data of the light environment detection module, and calculating the color temperature value of the current environment;
The intelligent white balance adjusting unit automatically adjusts the white balance setting of the multi-camera according to the calculated result of the ambient light color temperature analysis algorithm;
and the user interaction module is used for providing a user interaction interface, so that a user can easily set and finely adjust the white balance parameters, monitor the shooting effect in real time and optimize the white balance of the image according to the requirement.
Preferably, the ambient light detection module comprises a light source type, illumination intensity and direction detection component;
The ambient light detection module further comprises an ambient light intensity dynamic adjustment component for dynamically adjusting detection parameters according to the change of the ambient light intensity.
Preferably, the ambient light color temperature analysis algorithm comprises an image preprocessing module, a color space conversion module, a color temperature estimation module, a color histogram analysis module, a machine learning model module, an ambient light source identification module and a color temperature calibration module;
The image preprocessing module is used for denoising, white balance pre-adjustment and contrast enhancement of the images acquired by the multi-eye image acquisition unit;
A color space conversion module for converting image data from an RGB color space to a CIELAB or HSV color space;
a color temperature estimation module for estimating a color temperature from the chromaticity of a white or neutral gray region in the image;
The color histogram analysis module is used for analyzing the distribution condition of colors in the image;
a machine learning model module for predicting color temperature according to image features;
The environment light source identification module is used for identifying the main light source type in the environment;
and the color temperature calibration module is used for calibrating the estimated color temperature according to the identified light source type and the environmental characteristics.
Preferably, the image preprocessing module further comprises an automatic exposure control module, an automatic white balance control module and an edge enhancement algorithm;
The automatic exposure control module is used for dynamically adjusting the exposure of the image to adapt to different illumination conditions;
The automatic white balance control module is used for preliminarily adjusting the color temperature of the image;
An edge enhancement algorithm for improving the sharpness of edges in the image;
The color temperature estimation module comprises a chromaticity value mapping function and a color temperature estimation algorithm;
The chromaticity value mapping function is used for converting chromaticity values into corresponding color temperature values;
a color temperature estimation algorithm for calculating color temperatures of a plurality of regions in an image;
The ambient light source identification module can identify at least more than two light source types and adjust a color temperature estimation algorithm according to the light source types;
The color temperature calibration module adopts a self-adaptive algorithm, and adjusts the color temperature estimated value according to real-time feedback so as to adapt to the color temperature change under different environmental conditions.
Preferably, the intelligent white balance adjustment unit further comprises a gain control mechanism, and the gains of the three primary colors of red, green and blue are finely regulated and controlled to ensure that the white object presents the true color of the white object.
Preferably, the user interaction interface further has a real-time feedback function, and the white balance adjustment effect of the system is intuitively displayed to the user, so that the user can monitor the shooting effect in real time and correspondingly adjust according to the requirement.
The invention also provides an intelligent adjustment and optimization method for the white balance of the image of the multi-camera equipment, which is characterized by comprising the following steps:
capturing images of the environment where the multi-view camera equipment is located from different angles by utilizing a multi-view image acquisition unit to form a multi-angle view;
step b, evaluating the illumination conditions of the environment where the multi-camera is positioned through an ambient light detection module so as to acquire measurement data of illumination intensity, color temperature and direction;
Step c, processing and analyzing the image data captured by the multi-image acquisition unit and the measurement data provided by the ambient light detection module by adopting an ambient light color temperature analysis algorithm so as to calculate the color temperature value of the current environment;
Step d, according to the calculation result of the ambient light color temperature analysis algorithm, automatically adjusting the white balance setting of the multi-camera through an intelligent white balance adjusting unit;
And e, providing user operation and parameter adjustment functions through a user interaction module.
Preferably, the ambient light color temperature analysis algorithm in step c comprises the steps of:
Step c01, preprocessing the image acquired by the multi-eye image acquisition unit, including denoising, white balance pre-adjustment and contrast enhancement, so as to improve the accuracy of subsequent analysis;
Step c02, converting the image data from the RGB color space to the CIELAB or HSV color space;
Estimating a color temperature in the image, including analyzing chromaticity of a white or neutral gray region in the image;
step c04, analyzing a color histogram of the image, and determining the distribution condition of colors in the image;
step c05, predicting the color temperature by using a machine learning model;
Step c06, identifying main light source types in the environment so as to adapt to color temperature characteristics under different light sources;
and c07, performing color temperature calibration according to the identified light source type and the environmental characteristics, automatically adjusting the white balance setting of the multi-camera based on the color temperature calibration result, and finely regulating and controlling the gains of the three primary colors of red, green and blue.
Preferably, the machine learning model module in the step c05 uses a trained model, a Support Vector Machine (SVM) or a neural network to improve the accuracy of color temperature prediction.
The technical scheme of the invention has the following beneficial effects:
the invention detects the light condition and shooting environment of the multi-camera device in real time through the ambient light detection module, and intelligently adjusts the white balance parameters of the multi-camera according to the factors. The technical scheme can greatly improve the white balance adjusting precision and efficiency of the multi-camera equipment, thereby obtaining more accurate and real images.
According to the invention, through the ambient light detection module and the ambient light color temperature analysis algorithm, the illumination condition and the color temperature of the multi-camera equipment can be detected and analyzed in real time, so that the white balance adjustment with higher precision is realized.
The invention can improve the accuracy of color temperature estimation, namely, an ambient light color temperature analysis algorithm adopts an advanced image processing technology and a machine learning model, improves the accuracy and efficiency of color temperature measurement, reduces the requirement of manual intervention, and can more accurately estimate the ambient color temperature through the steps of comprehensive image preprocessing, color space conversion, color temperature estimation, color histogram analysis, machine learning model prediction, ambient light source identification, color temperature calibration and the like, thereby improving the accuracy of white balance adjustment.
The intelligent white balance adjusting unit automatically adjusts the white balance setting according to the analysis result, ensures that white objects can present the true color and luster of the white objects under different illumination conditions, enhances the authenticity and reliability of images, can instantly optimize the white balance parameters according to the dynamic change of the ambient illumination, and adapts to the color temperature change under different ambient conditions.
The user interaction module provides real-time feedback and visual parameter adjustment functions, so that a user can conveniently conduct personalized setting and optimization according to shooting effects monitored in real time.
The method can adapt to color temperature changes under different light sources and illumination conditions, automatically adjust white balance setting, and ensure that high-quality images can be obtained under various environments. The images captured by the multi-camera equipment at different angles can realize color consistency by the method, so that color deviation caused by inconsistent white balance is avoided, camera modules are allowed to be increased or decreased as required by a system, and the flexibility and expandability of the system are improved.
The invention effectively eliminates the possible chromatic aberration problem among different cameras and ensures the integral consistency of images by synchronously adjusting the white balance setting of a plurality of camera modules, is suitable for processing the mixed environment of a plurality of light sources, and can effectively identify and adapt to the color temperature characteristics of different light sources.
According to the invention, through an automatic intelligent adjustment flow, the need of manually adjusting the white balance is reduced, the operation efficiency is improved, and the operation difficulty of a user is reduced.
The method can ensure long-time stable operation and improve the reliability of the system through the real-time feedback and the self-adaptive adjustment mechanism.
The method is not only suitable for professional photography, but also suitable for various application scenes requiring high-quality image output, such as security monitoring, video conference, live broadcast and the like.
The integrated machine learning model can be continuously learned and optimized, and the intelligent level of white balance adjustment is improved along with the time.
The method of the invention ensures the safety and privacy of the user data when the image processing and analysis are carried out, and does not involve the leakage of any sensitive information.
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FIG. 1 is a schematic diagram of a control module according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples.
Referring to fig. 1, the present invention provides an intelligent white balance adjustment and optimization system for a multi-camera device, comprising:
a multi-view image acquisition unit for capturing images of the environment of the multi-view camera device and integrating the multi-angle views, which images are then transmitted to a back-end processing system for further analysis and processing.
The environment light detection module is used for evaluating the illumination conditions of the environment where the multi-camera is positioned and providing measurement data of illumination intensity, color temperature and direction;
the ambient light color temperature analysis algorithm is used for processing and analyzing the image data captured by the multi-view image acquisition unit and the measurement data of the light environment detection module, and calculating the color temperature value of the current environment;
The intelligent white balance adjusting unit automatically adjusts the white balance setting of the multi-camera according to the calculated result of the ambient light color temperature analysis algorithm;
And the user interaction module is used for setting and fine-tuning the white balance parameters by a user, so that the user can easily set and fine-tune the white balance parameters, monitor shooting effects in real time and optimize the white balance of the image according to the needs.
The white balance adjustment precision can be improved by the system, the color temperature value of the current environment can be accurately calculated by evaluating the illumination conditions of the environments where the multi-camera is positioned in real time and adopting an advanced ambient light color temperature analysis algorithm, so that the white balance adjustment precision is remarkably improved. The invention can enhance the adaptability of the system, the intelligent white balance adjusting unit can instantly optimize the white balance parameters according to the dynamically-changed environment illumination conditions, so that the system can adapt to various complex illumination environments and ensure the image quality. Compared with the traditional white balance adjusting method relying on an external sensor, the system has the advantages that the hardware requirement is reduced, the cost is reduced, the system structure is simplified, and the system complexity is reduced. The invention can promote user experience, wherein the user interaction module provides visual white balance parameter setting and fine adjustment functions and the capability of feeding back the white balance adjustment effect of the system in real time, thereby enhancing the operation convenience and satisfaction of users. The invention can optimize the image quality, the system ensures the authenticity and the accuracy of the color of the image captured under different illumination conditions through intelligent white balance adjustment, and the overall quality of the image is obviously optimized.
The system can be widely applied to the fields of security and protection monitoring, consumer electronics, industrial detection, medical imaging and the like, and a plurality of fields requiring high-quality images.
The invention can improve the stability and reliability of the system, and can maintain stable white balance adjustment performance under various environments due to the reduction of dependence on external sensors, thereby improving the reliability of the system.
The invention can support personalized setting, the user can adjust the white balance parameters according to own requirements and preferences, and the system supports personalized setting to meet the specific requirements of different users.
Further, the multi-view image acquisition unit comprises a plurality of camera modules and an image synchronization module, wherein each camera module is used for independently capturing images at different angles, and the image synchronization module is used for synchronizing image data captured by different camera modules. The camera modules of this embodiment are provided with independence in that each camera module is designed to operate independently and to be able to capture images of a scene from a respective angle. This independence allows the system to perform an all-round visual analysis of complex scenes, capturing more details and information. The image synchronization module of the embodiment has a coordination function, namely the image synchronization module is responsible for coordinating the work of each camera module, ensures the consistency of all captured image data in time, realizes the synchronization of the image data through high-precision time stamps or synchronization signals, and avoids errors caused by time sequence differences. The image synchronization module of this embodiment further includes a data fusion algorithm that can integrate multiple angles of image data into a single, comprehensive view or multiple useful views. The fusion not only improves the coverage of the scene, but also enhances the depth and detail of the image data, provides richer information for white balance analysis, and in order to realize accurate synchronization, the image synchronization module may comprise or be connected into a high-precision clock management system to ensure the accuracy of synchronization, wherein the clock management system may be an independent hardware device or a high-precision time source integrated in the system. The design of the image synchronization module considers the fault tolerance of the system, and the system can still operate even if part of camera modules fail. Through the redundancy design, the image synchronization module can be automatically switched to the standby camera module, and the continuity and stability of data are ensured. The image synchronization module in this embodiment is in close cooperation with the intelligent white balance adjustment unit, and accurate synchronization data provided by the synchronization module is a precondition that the white balance adjustment algorithm is accurately executed, and this cooperative work ensures that white balance adjustment can be performed based on the latest and most accurate image data.
Furthermore, the ambient light detection module comprises a light source type, illumination intensity and direction detection component, and accurate measurement of the parameters enables the system to evaluate the intensity and color temperature of the ambient light and provide necessary data support for subsequent white balance adjustment. The ambient light detection module further comprises an ambient light intensity dynamic adjustment component for dynamically adjusting detection parameters according to the change of the ambient light intensity. The light source type detection component is used for identifying the light source type in the environment, such as natural light, an incandescent lamp, a fluorescent lamp or an LED lamp, and can provide key information for white balance adjustment by analyzing the spectral characteristics of the light. The illumination intensity detection assembly is responsible for measuring the brightness level of ambient light, provides necessary illumination intensity data for white balance adjustment, adopts a high-sensitivity light sensor, and can cover a wide range from low illumination to high illumination. The illumination direction detection component is used to determine the orientation and angle of the light source, which is critical to understanding how light is distributed in the scene. By accurately measuring the illumination direction, the system can better simulate and adjust the white balance in the natural light environment. An ambient light intensity dynamic adjustment component: according to the real-time detected ambient light intensity change, the detection parameters of the ambient light detection module are dynamically adjusted, and the dynamic adjustment mechanism ensures that the system can maintain high-accuracy white balance adjustment under different illumination conditions. The environment light intensity dynamic adjustment component adopts a self-adaptive algorithm to adjust the sensitivity or the measurement range of the sensor in real time according to the change of the illumination intensity, and the self-adaptive capability enables the system to quickly respond to the abrupt change of the illumination condition, such as going from indoor to outdoor. The data collected by the ambient light detection module is required to be fused and processed to provide consistent and accurate illumination condition information, and the data fusion algorithm of the image synchronization module comprehensively considers the information of the type of the light source, the illumination intensity and the direction to provide comprehensive data support for white balance adjustment. The ambient light detection module is in close cooperation with the intelligent white balance adjustment unit, so that white balance adjustment is ensured to be based on the latest and most accurate ambient light detection data. This cooperative mechanism enables the system to achieve fast and accurate white balance adjustment.
Furthermore, the ambient light color temperature analysis algorithm comprises an image preprocessing module, a color space conversion module, a color temperature estimation module, a color histogram analysis module, a machine learning model module, an ambient light source identification module and a color temperature calibration module, wherein the image preprocessing module is used for denoising, pre-adjusting white balance and enhancing contrast of an acquired image, and the processing steps improve the image quality and provide more accurate image data for subsequent color analysis and color temperature estimation. The color space conversion module is used for converting the image data from RGB color space to CIELAB or HSV (hue, saturation and brightness) color space, and the conversion ensures that the mathematical processing of the color is more visual and effective, and is convenient for color temperature estimation and color histogram analysis. And the color temperature estimation module is used for estimating the color temperature according to the chromaticity of a white or neutral gray region in the image and providing basic data for white balance adjustment. The color histogram analysis module is used for analyzing the distribution situation of the colors in the image, particularly the proportion of red and blue components, and the color histogram provides statistical information of the color distribution of the image and is helpful for further calibrating the color temperature. The machine learning model module is used for predicting the color temperature according to the image characteristics, can learn and adapt to different images and illumination conditions, and improves the accuracy and the robustness of the color temperature prediction. The system comprises an ambient light source identification module, a color temperature estimation module and a color temperature estimation module, wherein the ambient light source identification module is used for identifying main light source types in the environment, such as sunlight, incandescent lamps, fluorescent lamps and the like, and by identifying the light source types, the system can apply corresponding color temperature characteristics to optimize the color temperature estimation result. The color temperature calibration module is used for calibrating the estimated color temperature according to the identified light source type and the environmental characteristics, and the color temperature characteristics and environmental factors of different light sources are considered in the calibration process, so that the accuracy of white balance adjustment is ensured.
The image preprocessing module further comprises an automatic exposure control module, an automatic white balance control module and an edge enhancement algorithm, wherein the automatic exposure control module is used for dynamically adjusting exposure of an image to adapt to different illumination conditions and adjusting exposure parameters of the image in real time so as to ensure that the image can keep optimal brightness and details under different illumination conditions, the automatic white balance control module is used for preliminarily adjusting the color temperature of the image to reduce the requirement of manual adjustment and provide a basis for subsequent accurate color temperature estimation, and the edge enhancement algorithm is used for improving the definition of edges in the image, and is helpful for the subsequent module to more accurately identify objects and contours in the image by enhancing the contrast of the edges in the image. In this embodiment, the edge enhancement algorithm works cooperatively with the automatic exposure and white balance control module to ensure that the color and brightness of the image remain balanced while the edges are enhanced. The user can adjust the parameters of the image preprocessing module through the user interaction module so as to adapt to specific shooting requirements or personal preferences. The image preprocessing module can integrate a scene recognition function, and automatically select the most suitable preprocessing parameters according to the characteristics of different scenes. The preprocessing module ensures consistency of image data, and provides high-quality input for the whole system, thereby improving accuracy of color temperature analysis.
Further, the color temperature estimation module comprises a chromaticity value mapping function and a color temperature estimation algorithm, wherein the chromaticity value mapping function is used for converting chromaticity values into corresponding color temperature values and is responsible for converting color information in an image into the chromaticity values so as to map the chromaticity values to specific color temperature values. By analyzing white or neutral gray areas in the image samples, the chromaticity value mapping function is able to quantify the chromaticity characteristics of the image and correspond it to known color temperature values. The color temperature estimation algorithm is used for calculating the color temperatures of a plurality of areas in the image, and calculates the color temperatures of the plurality of areas in the image by utilizing the output of the chromaticity value mapping function so as to obtain the comprehensive color temperature distribution condition, and comprehensively considers the chromaticity values of different areas in the image so as to reduce the influence of local abnormal values and improve the accuracy of color temperature estimation. Further, the color temperature estimation module is designed to take into account the multi-region analysis in that the color temperature estimation algorithm does not analyze only a single region of the image, but rather segments the image and analyzes multiple regions to obtain a more accurate average color temperature. Adaptive thresholding an algorithm can adaptively determine color thresholds for color temperature calculations to distinguish white, neutral gray, and other color regions in an image. Consistency of color space-prior to the mapping of the chrominance values, the image data is ensured in a unified color space to maintain consistency and accuracy of the color temperature estimation. Adaptability of illumination conditions the color temperature estimation algorithm takes into account the color performance under different illumination conditions, and can adapt to strong light, weak light or mixed illumination environments. The algorithm optimizes the calculation process to realize quick color temperature estimation and meet the requirement of real-time processing. And the color temperature estimation module is closely cooperated with the ambient light source identification module, and adjusts parameters of a color temperature estimation algorithm according to the identified light source type. User interaction-the user can adjust certain parameters of the color temperature estimation module through the user interaction module so as to adapt to specific shooting requirements or personal preferences. The environment light source identification module can identify at least more than two light source types and adjust a color temperature estimation algorithm according to the light source types, has strong identification capability, and can detect and distinguish at least more than two light source types, such as sunlight, incandescent lamps, fluorescent lamps, LED lamps and the like. By analyzing the spectral characteristics of the light source or specific color markings in the image, the module is able to accurately identify the type of light source and use its information for subsequent color temperature estimation. The color temperature calibration module adopts a self-adaptive algorithm, and adjusts the color temperature estimated value according to real-time feedback so as to adapt to the color temperature change under different environmental conditions. The color temperature calibration module improves the accuracy of color temperature estimation through continuous learning and optimization, and ensures that accurate white balance adjustment can be realized under different illumination conditions. The design of the ambient light source identification module and the color temperature calibration module also considers the following aspects that the self-adaptive learning mechanism is adopted by the color temperature calibration module, the self-learning and adjustment capabilities are realized by the color temperature calibration module, and the color temperature estimation algorithm can be continuously optimized according to historical data and user feedback. The ambient light source identification module and the color temperature calibration module are tightly integrated and cooperatively work with other system modules (such as an image preprocessing module and a color temperature estimation module) to form an efficient overall system.
Furthermore, the intelligent white balance adjusting unit also comprises a gain control mechanism, and the white object is ensured to present the true color by finely adjusting and controlling the gains of the three primary colors of red, green and blue. By precisely controlling the gains of the primary colors, the module can eliminate color shift and ensure that the white object keeps natural color under different illumination conditions. The gain control mechanism provides fine adjustment capability, allows the system to perform small step adjustment so as to realize smoother and natural white balance transition, is beneficial to avoiding color jump and ensuring continuity and visual comfort of images in the adjustment process, and has real-time response capability, and can quickly adapt to dynamic changes of illumination conditions, such as on-off of indoor illumination or brightness change of outdoor illumination. The unit ensures that the white object can present its true color under various lighting conditions. In addition, the intelligent white balance adjusting unit has real-time response capability, and can optimize white balance parameters in real time according to the dynamic change of the ambient illumination so as to maintain the color accuracy and the authenticity of the image. The intelligent white balance adjusting unit can identify different shooting scenes and automatically adjust gain control parameters according to scene characteristics so as to achieve the optimal white balance effect. And multi-region white balance, namely when different color temperature conditions possibly exist in different regions in the image, the gain control mechanism can respectively adjust the regions so as to realize harmony and unification of the whole image. The gain control mechanism works in conjunction with the image preprocessing module (including auto-exposure and auto-white balance control) to ensure effective white balance adjustment at an early stage of image capture. The intelligent white balance adjusting unit designs a feedback loop, performs self-optimization according to the effect of intelligent white balance adjustment, and continuously improves the accuracy and efficiency of white balance adjustment. In designing the gain control mechanism, the stability of the system is particularly considered, and reliable white balance adjustment can be ensured under long-time running or extreme conditions.
Furthermore, the user interaction interface also has a real-time feedback function, and the white balance adjustment effect of the system is intuitively displayed to the user, so that the user can monitor the shooting effect in real time and correspondingly adjust according to the requirement. The user interaction module is a key bridge for connecting a user and the system, provides an intuitive and easy-to-use control mode, and enables the user to monitor and adjust shooting effects through a real-time feedback function. The user interaction module has a real-time feedback function, can display the white balance adjustment effect of the system to a user in real time, and the user can intuitively see the comparison before and after the white balance adjustment and the real-time image effect under the current setting.
The user interaction module provides real-time image previewing, so that a user can monitor the current shooting effect, including color accuracy and overall image quality. The user can judge whether the white balance setting needs to be further adjusted according to the image effect of the real-time preview. The user interaction module provides intuitive control options that allow the user to adjust white balance parameters, such as gain control, color temperature shift, etc., as desired.
The design of the user interaction module in this embodiment also considers the aspect that the user can set the layout of the interface and the type of information displayed, such as color temperature value, gain level, etc., according to personal preferences. The interface provides a plurality of preset white balance modes, and is suitable for different shooting environments, such as sunlight, cloudy days, indoor and the like. The user can switch between automatic white balance adjustment and manual adjustment as desired. The interface records the user's adjustment history, enabling the user to trace back and compare effects under different settings. The interface integrates auxiliary tools, such as color analyzers, exposurers, etc., to help the user better understand the current image state. On-line help and user guidance are provided to help the user understand how to use the interface and adjust white balance. The user interaction module design allows for multi-platform use, providing a good user experience, both on professional monitors, notebook computers, and mobile devices. The user can provide feedback through the interface, the system optimizes according to the feedback of the user, and the intelligence and the accuracy of white balance adjustment are improved. The interface adopts a data visualization technology, complex white balance data is displayed in a graphical mode, and the readability of information is improved.
The invention also provides an intelligent adjustment and optimization method for the white balance of the image of the multi-camera equipment, which comprises the following steps:
capturing images of the environment where the multi-camera device is located from different angles by using a multi-view image acquisition unit and integrating environmental images of multiple angles;
environmental images from different angles are synchronously captured by utilizing a multi-view image acquisition unit so as to obtain comprehensive illumination and color information, and the images are integrated to form a comprehensive view, so that a data basis is provided for subsequent illumination condition evaluation and color temperature analysis.
And b, evaluating the illumination conditions of the environment where the multi-camera is positioned by using an ambient light detection module, wherein the illumination conditions comprise the type of a light source, illumination intensity and direction, and providing key ambient parameters for intelligent white balance adjustment by using an evaluation result so as to ensure that the white balance adjustment can adapt to actual illumination conditions.
And c, processing and analyzing the image data captured by the multi-image acquisition unit and the measurement data provided by the ambient light detection module by using an ambient light color temperature analysis algorithm to calculate the color temperature value of the current environment, wherein the algorithm can provide a more accurate ambient color temperature value by analyzing the image data and the illumination measurement data, so that the accuracy of white balance adjustment is improved. The automated algorithm reduces the need for manual intervention, saves time and reduces the likelihood of operational errors. By combining the image data and the illumination measurement data, the algorithm can more comprehensively understand the environment illumination condition, and the reliability of color temperature estimation is improved. The algorithm can adapt to different illumination environments and condition changes, and can ensure that good white balance adjustment can be realized in various environments. The ambient light color temperature analysis algorithm is designed to be capable of rapidly processing data, realizing real-time or near real-time color temperature calculation, and meeting the requirement of rapid response. Accurate color temperature calculation helps to optimize the overall quality of the image, particularly in a multi-camera system, ensuring color consistency and authenticity. Advanced image processing and analysis technology, including machine learning model, is adopted to ensure the advancement and accuracy of the algorithm in color temperature estimation. By providing accurate color temperature measurement and adjustment, the algorithm enhances the user's experience when using the multi-view camera device. The method is suitable for various scenes, namely the flexibility and the adaptability of the algorithm are suitable for various shooting scenes from indoor to outdoor and from sunlight to artificial light sources. And c, calculating a color temperature value of the current environment according to the illumination condition data obtained in the step b by adopting an ambient light color temperature analysis algorithm. The ambient light color temperature analysis comprises the substeps of image preprocessing, color space conversion, color temperature estimation and the like, and the accuracy of color temperature values is ensured.
And d, according to the calculation result of the color temperature analysis algorithm of the ambient light, the intelligent white balance adjustment unit automatically adjusts the white balance setting, wherein the adjustment comprises gain control, color temperature compensation and the like so as to ensure the color authenticity of the white object under different illumination conditions.
Step e provides the user operation and parameter adjustment functions through the user interaction module. The user interaction module provides an intuitive operation interface, allowing a user to perform manual operation and parameter adjustment. The real-time feedback function enables the user to monitor the shooting effect and make corresponding adjustments as needed to achieve optimal image quality.
Further, the environmental light color temperature analysis algorithm in the step c comprises the following steps:
step c01, preprocessing the acquired image, including denoising, white balance pre-adjustment and contrast enhancement, so as to improve the accuracy of subsequent analysis, denoising the acquired image, reducing image noise and improving image quality. And performing white balance pre-adjustment, performing preliminary color temperature correction on the image, and providing a basis for subsequent color temperature estimation. Contrast enhancement improves the contrast of the bright and dark areas in the image, so that the color edges are clearer.
Step c02, converting the image data from the RGB color space to a CIELAB or HSV color space, which is more suitable for color analysis and color temperature estimation.
Step c03, estimating the color temperature in the image, including analyzing the chromaticity of white or neutral gray areas in the image, the chromaticity of these areas being directly related to the ambient color temperature.
And step c04, analyzing the color histogram of the image, determining the distribution condition of the colors in the image, and providing statistical data support for color temperature estimation.
And step c05, predicting the color temperature by using a machine learning model, and predicting the color temperature according to the image characteristics to improve the accuracy of color temperature estimation. The machine learning model module employs a trained model, support Vector Machine (SVM) or neural network to improve accuracy of color temperature predictions.
Step c06, identifying the main light source type in the environment to adapt to the color temperature characteristics under different light sources, ,, such as natural light, incandescent lamp, etc., because different light sources have different color temperature characteristics.
And c07, performing color temperature calibration according to the identified light source type and the environmental characteristics, automatically adjusting the white balance setting of the multi-camera based on the color temperature calibration result, and finely regulating and controlling the gains of the three primary colors of red, green and blue.
As can be seen from the above embodiments, the present invention detects the light condition and the shooting environment of the multi-camera device in real time through the ambient light detection module, and intelligently adjusts the white balance parameters of the multi-camera according to these factors. The technical scheme can greatly improve the white balance adjusting precision and efficiency of the multi-camera equipment, thereby obtaining more accurate and real images.
According to the invention, through the ambient light detection module and the ambient light color temperature analysis algorithm, the illumination condition and the color temperature of the multi-camera equipment can be detected and analyzed in real time, so that the white balance adjustment with higher precision is realized.
The invention can improve the accuracy of color temperature estimation, namely, an ambient light color temperature analysis algorithm adopts an advanced image processing technology and a machine learning model, improves the accuracy and efficiency of color temperature measurement, reduces the requirement of manual intervention, and can more accurately estimate the ambient color temperature through the steps of comprehensive image preprocessing, color space conversion, color temperature estimation, color histogram analysis, machine learning model prediction, ambient light source identification, color temperature calibration and the like, thereby improving the accuracy of white balance adjustment.
The intelligent white balance adjusting unit automatically adjusts the white balance setting according to the analysis result, ensures that white objects can present the true color and luster of the white objects under different illumination conditions, enhances the authenticity and reliability of images, can instantly optimize the white balance parameters according to the dynamic change of the ambient illumination, and adapts to the color temperature change under different ambient conditions.
The user interaction module provides real-time feedback and visual parameter adjustment functions, so that a user can conveniently conduct personalized setting and optimization according to shooting effects monitored in real time.
The method can adapt to color temperature changes under different light sources and illumination conditions, automatically adjust white balance setting, and ensure that high-quality images can be obtained under various environments. The images captured by the multi-camera equipment at different angles can realize color consistency by the method, so that color deviation caused by inconsistent white balance is avoided, camera modules are allowed to be increased or decreased as required by a system, and the flexibility and expandability of the system are improved.
The invention effectively eliminates the possible chromatic aberration problem among different cameras and ensures the integral consistency of images by synchronously adjusting the white balance setting of a plurality of camera modules, is suitable for processing the mixed environment of a plurality of light sources, and can effectively identify and adapt to the color temperature characteristics of different light sources.
According to the invention, through an automatic intelligent adjustment flow, the need of manually adjusting the white balance is reduced, the operation efficiency is improved, and the operation difficulty of a user is reduced.
The method can ensure long-time stable operation and improve the reliability of the system through the real-time feedback and the self-adaptive adjustment mechanism.
The method is not only suitable for professional photography, but also suitable for various application scenes requiring high-quality image output, such as security monitoring, video conference, live broadcast and the like.
The integrated machine learning model can be continuously learned and optimized, and the intelligent level of white balance adjustment is improved along with the time.
The method of the invention ensures the safety and privacy of the user data when the image processing and analysis are carried out, and does not involve the leakage of any sensitive information.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (10)
1. An intelligent white balance adjustment and optimization system for a multi-camera device, comprising:
the multi-view image acquisition unit is used for capturing images of the environment where the multi-view camera equipment is located from different angles to form a multi-angle view;
The environment light detection module is used for evaluating the illumination conditions of the environment where the multi-camera is positioned and providing measurement data of illumination intensity, color temperature and direction;
the ambient light color temperature analysis algorithm is used for processing and analyzing the image data captured by the multi-view image acquisition unit and the measurement data of the light environment detection module, and calculating the color temperature value of the current environment;
The intelligent white balance adjusting unit automatically adjusts the white balance setting of the multi-camera according to the calculated result of the ambient light color temperature analysis algorithm;
and the user interaction module is used for providing a user interaction interface, so that a user can easily set and finely adjust the white balance parameters, monitor the shooting effect in real time and optimize the white balance of the image according to the requirement.
2. The intelligent white balance adjustment and optimization system of a multi-camera device of claim 1, wherein the multi-camera image acquisition unit comprises a plurality of camera modules, an image synchronization module, wherein each camera module independently captures images at different angles, the image synchronization module being configured to synchronize image data captured by different camera modules.
3. The intelligent white balance adjustment and optimization system of a multi-camera device of claim 1, wherein the ambient light detection module comprises a light source type, illumination intensity, and direction detection assembly;
The ambient light detection module further comprises an ambient light intensity dynamic adjustment component for dynamically adjusting detection parameters according to the change of the ambient light intensity.
4. The intelligent white balance adjustment and optimization system of a multi-camera device of claim 1, wherein the ambient light color temperature analysis algorithm comprises an image preprocessing module, a color space conversion module, a color temperature estimation module, a color histogram analysis module, a machine learning model module, an ambient light source identification module, and a color temperature calibration module;
The image preprocessing module is used for denoising, white balance pre-adjustment and contrast enhancement of the images acquired by the multi-eye image acquisition unit;
A color space conversion module for converting image data from an RGB color space to a CIELAB or HSV color space;
a color temperature estimation module for estimating a color temperature from the chromaticity of a white or neutral gray region in the image;
The color histogram analysis module is used for analyzing the distribution condition of colors in the image;
a machine learning model module for predicting color temperature according to image features;
The environment light source identification module is used for identifying the main light source type in the environment;
and the color temperature calibration module is used for calibrating the estimated color temperature according to the identified light source type and the environmental characteristics.
5. The intelligent white balance adjustment and optimization system of the multi-camera device according to claim 4, wherein the image preprocessing module further comprises an automatic exposure control module, an automatic white balance control module and an edge enhancement algorithm;
The automatic exposure control module is used for dynamically adjusting the exposure of the image to adapt to different illumination conditions;
The automatic white balance control module is used for preliminarily adjusting the color temperature of the image;
An edge enhancement algorithm for improving the sharpness of edges in the image;
The color temperature estimation module comprises a chromaticity value mapping function and a color temperature estimation algorithm;
The chromaticity value mapping function is used for converting chromaticity values into corresponding color temperature values;
a color temperature estimation algorithm for calculating color temperatures of a plurality of regions in an image;
The ambient light source identification module can identify at least more than two light source types and adjust a color temperature estimation algorithm according to the light source types;
The color temperature calibration module adopts a self-adaptive algorithm, and adjusts the color temperature estimated value according to real-time feedback so as to adapt to the color temperature change under different environmental conditions.
6. The intelligent white balance adjustment and optimization system of a multi-camera device according to claim 1, wherein the intelligent white balance adjustment unit further comprises a gain control mechanism, wherein the white object is ensured to present the true color thereof by finely adjusting the gains of the three primary colors of red, green and blue.
7. The intelligent white balance adjusting and optimizing system of the multi-camera equipment according to claim 1, wherein the user interaction interface further has a real-time feedback function, and the white balance adjusting effect of the system is intuitively displayed to a user, so that the user can monitor the shooting effect in real time and correspondingly adjust the shooting effect according to the requirement.
8. The intelligent adjustment and optimization method for the white balance of the image of the multi-camera equipment is characterized by comprising the following steps of:
capturing images of the environment where the multi-view camera equipment is located from different angles by utilizing a multi-view image acquisition unit to form a multi-angle view;
step b, evaluating the illumination conditions of the environment where the multi-camera is positioned through an ambient light detection module so as to acquire measurement data of illumination intensity, color temperature and direction;
Step c, processing and analyzing the image data captured by the multi-image acquisition unit and the measurement data provided by the ambient light detection module by adopting an ambient light color temperature analysis algorithm so as to calculate the color temperature value of the current environment;
Step d, according to the calculation result of the ambient light color temperature analysis algorithm, automatically adjusting the white balance setting of the multi-camera through an intelligent white balance adjusting unit;
And e, providing user operation and parameter adjustment functions through a user interaction module.
9. The method for intelligently adjusting and optimizing the white balance of an image of a multi-camera device according to claim 8, wherein the ambient light color temperature analysis algorithm in the step c comprises the following steps:
Step c01, preprocessing the image acquired by the multi-eye image acquisition unit, including denoising, white balance pre-adjustment and contrast enhancement, so as to improve the accuracy of subsequent analysis;
Step c02, converting the image data from the RGB color space to the CIELAB or HSV color space;
Estimating a color temperature in the image, including analyzing chromaticity of a white or neutral gray region in the image;
step c04, analyzing a color histogram of the image, and determining the distribution condition of colors in the image;
step c05, predicting the color temperature by using a machine learning model;
Step c06, identifying main light source types in the environment so as to adapt to color temperature characteristics under different light sources;
and c07, performing color temperature calibration according to the identified light source type and the environmental characteristics, automatically adjusting the white balance setting of the multi-camera based on the color temperature calibration result, and finely regulating and controlling the gains of the three primary colors of red, green and blue.
10. The method for intelligently adjusting and optimizing the white balance of an image of a multi-camera device according to claim 9, wherein the machine learning model module in the step c05 adopts a trained model, a support vector machine or a neural network to improve the accuracy of color temperature prediction.
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