WO2020062393A1 - 一种基于机器学习的初始数据处理方法和系统 - Google Patents
一种基于机器学习的初始数据处理方法和系统 Download PDFInfo
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- WO2020062393A1 WO2020062393A1 PCT/CN2018/112594 CN2018112594W WO2020062393A1 WO 2020062393 A1 WO2020062393 A1 WO 2020062393A1 CN 2018112594 W CN2018112594 W CN 2018112594W WO 2020062393 A1 WO2020062393 A1 WO 2020062393A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
- G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
- G06V10/141—Control of illumination
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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
- H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time
Definitions
- the invention relates to the field of data processing, in particular to a method and system for initial data processing based on machine learning.
- the back-end processing module As the input of the back-end processing module, the quality of the camera's imaging quality is very important.
- the traditional processing flow is to directly send the initial data to the ISP (Image Signal Processing) module, and then output the YUV or RGB image after processing by the ISP module.
- This processing flow can achieve better results in normal environments, but the image output under certain harsh or extreme conditions is not ideal.
- the back-end processing module also needs to add specific modules to process the signals separately.
- the purpose of the present invention is to address the problems mentioned above, in order to improve the imaging effect of the camera in extreme environments and reduce the back-end processing module, a new ISP processing method is proposed.
- Data processing module Raw data is sent to the ISP module after passing through the raw data processing module.
- the present invention provides an initial data processing method based on machine learning for recovering original scene data for extreme environments, including the following specific steps:
- S1 Collect several scene data, and use front-end sensors to collect scene data to obtain several sets of scene data pairs; the data pairs include high-quality initial data and low-quality initial data of the same scene;
- S5 Perform ISP image processing on the obtained high-quality initial data, and output to obtain a high-quality scene image.
- the scenes are harsh or extreme natural scenes, including low-light scenes, under-exposed scenes, and over-exposed scenes.
- the short exposure + long exposure method is adopted: first, the fixed camera is used to shoot the short-exposed Raw image, and then the camera is not moved to take the long-exposed Raw image;
- the data pair acquisition is performed according to the data pair acquisition of the underexposure scene.
- step S2 since each set of data contains high-quality initial data with good effect and low-quality initial data with poor effect in the same scene, the collected initial data pair is sent to a machine learning model to train the learning model.
- the model mapping relationship between low-quality initial data and high-quality initial data is used to obtain a trained machine learning model.
- the machine learning model is a learning model of a deep convolutional neural network, and network parameters of the deep convolutional neural network are consistent with U-Net.
- the present invention also provides an initial data processing system based on machine learning for recovering original scene data for extreme environments, including the following specific modules:
- a learning data acquisition module is used to collect several scene data, and use front-end sensors to collect scene data to obtain several sets of scene data pairs; the data pairs include high-quality initial data and low-quality initial data of the same scene;
- a model training module which is used to train and construct a machine learning model by using several sets of collected scene data pairs
- the to-be-processed data acquisition module is used to collect the initial data of the scene to be processed, and use the front-end sensors to collect the data of the severe or extreme scene to be processed to obtain the low-quality initial data corresponding to the scene;
- a data recovery module which is used to input the obtained low-quality initial data into a trained machine learning model, and output the corresponding high-quality scene data after processing by the model;
- An image processing module is configured to perform ISP image processing on the recovered high-quality initial data to output and obtain a high-quality scene image.
- the scenes are harsh or extreme natural scenes, including low-light scenes, under-exposed scenes, and over-exposed scenes.
- the short exposure + long exposure method is adopted: first, the fixed camera is used to shoot the short-exposed Raw image, and then the camera is not moved to take the long-exposed Raw image;
- the data pair acquisition is performed according to the data pair acquisition of the underexposure scene.
- each set of data contains high-quality initial data that performs well in the same scene and low-quality initial data that performs poorly
- the collected initial data pairs are sent to a machine learning model and the learning model is processed.
- the model mapping relationship between low-quality initial data and high-quality initial data is used to obtain a trained machine learning model.
- the machine learning model is a learning model of a deep convolutional neural network, and network parameters of the deep convolutional neural network are consistent with U-Net.
- the initial data processing method based on machine learning provided by the present invention has good universality for different extreme environments, and does not need to add different specific processing modules, which is suitable for various types of data processing.
- the initial data processing method based on machine learning provided by the present invention by restoring the initial data, retains various detailed information in the initial data, and uses the initial data retaining the detailed information for ISP image processing to obtain high quality Output image.
- FIG. 1 is a flowchart of initial data processing based on machine learning.
- Figure 2 is a short exposure RAW image in a low light scene.
- Figure 3 is a long exposure RAW image in a low light scene.
- Figure 4 shows the output of the high-quality RGB image after ISP processing.
- This embodiment is an initial data processing method based on machine learning.
- machine learning or deep learning is used to recover high-quality scene data from corresponding RAW images and use them for subsequent images.
- data processing The scene includes a natural environment, a face, or other scenes.
- the method includes the following specific steps:
- S1. Collect scene data, and use front-end sensors to collect scene data to obtain several sets of scene data pairs, where the data pairs include high-quality initial data and low-quality initial data of the same scene;
- the processing method provided in this embodiment is mainly directed to harsh or extreme natural scenes, and mainly considers low-illumination, under-exposure, over-exposure, etc., different extreme scenes have different data collection methods.
- the short exposure + long exposure method is adopted: Assume that the long exposure image is the reference image and the short exposure image is the input to-be-processed image.
- the specific acquisition method is: first, fix the camera and shoot the short exposure Raw image, as shown in Figure 2, and then keep the camera still, take a long exposure Raw image, as shown in Figure 3;
- data pair acquisition is performed in the manner of data collection of underexposure scenes.
- the above original scene data refers to original raw image data, that is, the data collected and output is the raw data corresponding to the scene.
- each set of data contains high-quality initial data that performs well in the same scene and low-quality initial data that performs poorly, these initial data pairs can be sent to the machine learning model.
- the low-quality initial data is used.
- Model mapping relationship between the data and high-quality initial data to obtain a trained machine learning model;
- the machine learning model is a deep convolutional neural network. Since the design of the convolutional neural network model needs to follow the same principles as the input data and output data resolution size, number of channels, and data format, as long as the deep neural network that meets this condition can be used in the processing method of this embodiment, this The embodiment does not specifically limit this.
- U-Net can be selected to design the convolutional neural network, and the detailed network design parameters are consistent with U-Net.
- ISP image processing is performed on the obtained high-quality initial data to obtain a high-quality scene image, as shown in FIG. 4.
- This embodiment is an initial data processing system based on machine learning. For a low-quality scene image, a machine learning model or a deep learning model is used to recover a high-quality scene image from the corresponding low-quality initial data.
- the system includes the following specific processing modules:
- a learning data acquisition module is used to collect several scene data, and use front-end sensors to collect scene data to obtain several sets of scene data pairs, where the data pairs include high-quality initial data and low-quality initial data of the same scene;
- the processing system provided in this embodiment is mainly directed to harsh or extreme natural scenes, and mainly considers low illumination, underexposure, overexposure, etc., the data collection methods corresponding to different extreme scenes are different.
- the short exposure + long exposure method is adopted: Assume that the long exposure image is the reference image and the short exposure image is the input pending image.
- the specific acquisition method is: first fix the camera and shoot the short exposure Raw Image, then keep the camera still and take long exposure Raw images;
- data pair acquisition is performed in the manner of data collection of underexposure scenes.
- the above-mentioned initial data refers to initial raw image data, that is, the data collected and output is scene raw data.
- a model training module which is used to train and construct a machine learning model by using several sets of collected scene data pairs
- each set of data contains high-quality initial data that performs well in the same scene and low-quality initial data that performs poorly, these initial data pairs can be sent to the machine learning model.
- the low-quality initial data is used.
- Model mapping relationship between the data and high-quality initial data to obtain a trained machine learning model;
- the machine learning model is a deep convolutional neural network. Since the design of the convolutional neural network model needs to follow the same principles as the input data and output data resolution size, number of channels, and data format, as long as the deep neural network that meets this condition can be used in the processing method of this embodiment, this The embodiment does not specifically limit this.
- U-Net can be selected to design the convolutional neural network, and the detailed network design parameters are consistent with U-Net.
- the to-be-processed data acquisition module is used to collect the initial data of the scene to be processed, and use the front-end sensors to collect the data of the severe or extreme scene to be processed to obtain the low-quality initial data corresponding to the scene;
- a data recovery module which is used to input the low-quality initial data to be processed into a trained machine learning model, and output the corresponding high-quality scene data after the model is processed;
- An image processing module is configured to perform ISP image processing on the recovered high-quality initial data to output and obtain a high-quality scene image.
- the invention is not limited to the foregoing specific embodiments.
- the invention extends to any new feature or any new combination disclosed in this specification, and to any new method or process step or any new combination disclosed.
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Abstract
Description
Claims (10)
- 一种基于机器学习的初始数据处理方法,用于针对极端环境进行原始场景数据恢复,其特征在于,包括以下具体步骤:S1,采集若干场景数据,利用前端的传感器对场景数据进行采集,得到若干组场景数据对;所述数据对包括同一场景的高质量初始数据和低质量初始数据;S2,利用采集的若干组场景数据对进行训练和构建机器学习模型;S3,采集待处理的场景初始数据,利用前端传感器对待处理的恶劣或极端场景数据进行采集,得到该场景对应的待处理的低质量初始数据;S4,将获取的低质量初始数据输入经过训练好的机器学习模型,通过模型处理后输出得到对应的高质量的场景数据;S5,将得到的高质量初始数据进行ISP图像处理,输出获得高质量的场景图像。
- 如权利要求1所述的一种基于机器学习的初始数据处理方法,其特征在于,所述场景为恶劣或极端的自然场景,包括低照度场景、欠曝场景、过曝场景。
- 如权利要求2所述的一种基于机器学习的初始数据处理方法,其特征在于,不同的极端场景对应的数据对采集方式不同;对于低照度场景的数据对采集,采用短曝光+长曝光的方式:首先固定摄像头拍摄短曝光Raw图像,然后保持摄像头不动,拍摄长曝光Raw图像;对于欠曝光场景的数据对采集:首先固定摄像头拍摄该场景正常光照条件下的Raw图像,然后调节环境亮度,使场景中的目标物体处于欠曝光环境,并拍摄对应的Raw图像;对于过曝光场景的数据对采集,按照欠曝光场景数据对采集的方式进行数据对采集。
- 如权利要求1所述的一种基于机器学习的初始数据处理方法,其特征在于,所述步骤S2中,由于每组数据对包含同一场景的效果较好的高质量初始数据和效果较差的低质量初始数据,将采集的初始数据对送入机器学习模型对学习模型进行训练,利用低质量初始数据和高质量初始数据之间的模型映射关系,得到训练好的机器学习模型。
- 如权利要求1所述的一种基于机器学习的初始数据处理方法,其特征在于,所述机器学习模型为深度卷积神经网络的学习模型,所述深度卷积神经网络的网络参数与U-Net保持一致。
- 一种基于机器学习的初始数据处理系统,用于针对极端环境进行原始场景数据恢复,其特征在于,包括以下具体模块:学习数据采集模块,用于采集若干场景数据,利用前端的传感器对场景数据进行采集,得到若干组场景数据对;所述数据对包括同一场景的高质量初始数据和低质量初始数据;模型训练模块,用于利用采集的若干组场景数据对进行训练和构建机器学习模型;待处理数据采集模块,用于采集待处理的场景初始数据,利用前端传感器对待处理的恶劣或极端场景数据进行采集,得到该场景对应的待处理的低质量初始数据;数据恢复模块,用于将获取的低质量初始数据输入经过训练好的机器学习模型,通过模型处理后输出得到对应的高质量的场景数据;图像处理模块,用于将恢复得到的高质量初始数据进行ISP图像处理,输出获得高质量的场景图像。
- 如权利要求6所述的一种基于机器学习的初始数据处理系统,其特征在于,所述场景为恶劣或极端的自然场景,包括低照度场景、欠曝场景、过曝场景。
- 如权利要求7所述的一种基于机器学习的初始数据处理系统,其特征在于,不同的极端场景对应的数据对采集方式不同;对于低照度场景的数据对采集,采用短曝光+长曝光的方式:首先固定摄像头拍摄短曝光Raw图像,然后保持摄像头不动,拍摄长曝光Raw图像;对于欠曝光场景的数据对采集:首先固定摄像头拍摄该场景正常光照条件下的Raw图像,然后调节环境亮度,使场景中的目标物体处于欠曝光环境,并拍摄对应的Raw图像;对于过曝光场景的数据对采集,按照欠曝光场景数据对采集的方式进行数据对采集。
- 如权利要求6所述的一种基于机器学习的初始数据处理方法,其特征在于,在所述模型训练模块中,由于每组数据对包含同一场景的效果较好的高质量初始数据和效果较差的低质量初始数据,将采集的初始数据对送入机器学习模型对学习模型进行训练,利用低质量初始数据和高质量初始数据之间的模型映射关系,得到训练好的机器学习模型。
- 如权利要求6所述的一种基于机器学习的初始数据处理方法,其特征在于,所述机器学习模型为深度卷积神经网络的学习模型,所述深度卷积神经网络的网络参数与U-Net保持一致。
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