Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Boe Technology Groupco., Ltd.

Guannan Chen

Lijie Zhang

Yanhong Wu

Dan Zhu

Ran Duan

Hanwen Liu

Yan Gao

Mengdi Sun

Wenbin Chen

Yunhua Lu

Pablo Navarrete Michelini

Tingting Wang

Xian Wang

Congrui Wu

Honghong Jia

Fengshuo Hu

Jingru Wang

Feng Hou

A method for training an image processing model includes: inputting a low dynamic range image into a first initial image processing model, so that a high dynamic range reconstruction processing is performed on the low dynamic range image to generate a first high dynamic range image; inputting the low dynamic range image into a second initial image processing model to generate a first coefficient; generating a second high dynamic range image according to the first high dynamic range image and the first coefficient; generating a loss function according to data pairs of the second high dynamic range image and a real high dynamic range image, the real high dynamic range image corresponding to the low dynamic range image; and training the first initial image processing model and the second initial image processing model with the loss function.

Method for training image processing model, and method for generating high dynamic range image

Provided is a method for processing a video. The method for processing the video can segment an initial video into a plurality of video clips, and calculate color temperatures of the plurality of video clips. For each target video clip in the plurality of video clips, the method can determine a color temperature adjustment coefficient of the target video clip based on a color temperature of the target video clip and a color balance correction coefficient thereof, and adjust a color temperature of at least one video frame in the target video clip by using the color temperature adjustment coefficient.

Method and apparatus for processing video, and display device

The present disclosure provides a display method for a splicing display screen, which belongs to the technical field of image display, wherein the splicing display screen includes a plurality of display panels spliced with each other, and the display method includes: determining initial gray-scale compensation data according to first gray-scale data for each pixel in current frame of image data and a pre-generated gray-scale compensation data table; acquiring temperature influence data of current frame of image data, and filtering each temperature influence data according to preset filter parameter information to obtain a gray-scale compensation coefficient; determining target gray-scale compensation data according to the gray-scale compensation coefficient and the initial gray-scale compensation data; and compensating a gray scale of the current frame of image data according to the target gray-scale compensation data to obtain compensated frame of image data.

Display method and device for splicing display screen, computer device and storage medium

Disclosed are a display device system and a method for adaptively enhancing an image quality. The method includes: acquiring an original image, performing iterative detail enhancement processing on the original image by utilizing at least one filter function containing iteratively-updated filter parameters, in a case that a difference between a quality evaluation score of the input image and a quality evaluation score of the detail-enhanced image of the iiteration satisfies an iteration condition, obtaining filter parameters of an (i+1)iteration by updating the filter parameters of the iiteration, and continuing to perform detail enhancement processing on the input image by utilizing at least one filter function containing the filter parameters of the (i+1)iteration, until a difference between the quality evaluation score of the input image and a quality evaluation score of a detail-enhanced image of the (i+1)iteration satisfies the iteration condition.

Display device system and method for adaptively enhancing image quality

Provided is a method for processing an image. The method includes: acquiring a first original image to be processed; and acquiring a degraded image by performing down-sampling processing and blurring processing on the first original image according to a configured down-sampling processing algorithm and a blurring parameter; wherein the down-sampling processing algorithm includes a plurality of sampling steps.

Method and device for processing image, method for training image processing model, and storage medium

A computer-implemented image-processing method is provided. The computer-implemented image-processing method includes obtaining a pair of training samples including a training image having a first degree of sharpness and a reference image having a second degree of sharpness, the second degree greater than the first degree, at least portions of the training image and the reference image in a same pair having same contents; inputting the training image to the image-enhancing convolutional neural network to generate a training enhanced image; inputting the training enhanced image into an edge detector; generating, by the edge detector, a plurality of first edge maps; inputting the reference image into the edge detector; generating, by the edge detector, a plurality of second edge maps; and tuning parameters in the image-enhancing convolutional neural network to minimize at least the one or more first losses and a second loss.

Computer-implemented image-processing method, image-enhancing convolutional neural network, and computer product

A computer-implemented method is provided. The computer-implemented method includes inputting a low-resolution image and a plurality of high-resolution images into a feature extractor, the low-resolution image and the plurality of high-resolution images including images of a target object; obtaining, by the feature extractor, feature maps of the low-resolution image and the plurality of high-resolution images; comparing similarities between the feature maps of the low-resolution image and the plurality of high-resolution images; obtaining selected feature maps of one or more selected high-resolution images of the plurality of high-resolution images most similar to the low-resolution image; inputting the selected feature maps into a generator to output a repair image; enhancing the low-resolution image using a pre-processing image enhancing process to generate an enhanced image; and morphing the repair image with the enhanced image.

Computer-implemented method, apparatus, and computer-program product

An image processing method and an image processing device are provided. The image processing method includes performing at least one of a scratch repairing step, a dead point repairing step, a denoising step and a color cast correcting step on a to-be-processed video frame. According to the embodiments of the present disclosure, it is able to repair a scratch and a dead point, remove a noise and/or correct color cast for a video frame, thereby to improve a display effect of the video frame.

Image processing method for performing color migration to correction image according to reference image, and obtaining first scratch image and/or second scratch image so as to obtain scratch repairing image

An image processing method, an apparatus, an electronic device and a non-transient computer-readable storage medium. The image processing method includes: acquiring an original image; performing a fuzzy processing to the original image to obtain a fuzzy image; performing a high-dynamic-range image to the original image by using a first network model obtained by pre-training, to obtain a first characteristic matrix, wherein the first network model includes a dense residual module and a gate-control-channel conversion module; obtaining an auxiliary characteristic matrix of the original image according to the fuzzy image, wherein the auxiliary characteristic matrix includes detail information of the original image and/or low-frequency information of the original image; obtaining a target image according to the first characteristic matrix and the auxiliary characteristic matrix.

Image processing method, apparatus, electronic device and storage medium

A video enhancement method and apparatus, an electronic device, and a storage medium are described. The method comprises: extracting features from M frames of images, so as to obtain at least one first-scale image feature (S); for each first-scale image feature, performing N-level down-sampling processing on the first-scale image feature, so as to obtain a second-scale image feature (S); performing N-level up-sampling processing on the second-scale image feature, so as to obtain a third-scale image feature (S), wherein the input of ith-level up-sampling processing is an image feature obtained after performing superimposition processing on the output of (N+1−i)th-level down-sampling processing and the output of (i−1)th-level up-sampling processing, and the multiple of jth-level up-sampling is the same as the multiple of (N+1−j)th-level down-sampling; and performing superimposition processing on the third-scale image feature and the first-scale image feature.

Video enhancement method and apparatus, and electronic device and storage medium

The present disclosure relates to the field of image processing technology, and in particular, to image encoding, decoding methods and devices, an encoder-decoder. The method includes: acquiring a visual saliency heat map of an image of a current frame, and filtering, by using the visual saliency heat map of the image of the current frame, the image of the current frame to obtain a target image; acquiring, by using the target image and an input image of a next frame, a motion estimation vector and a target prediction image of the input image of the next frame; and encoding a difference image between the input image of the next frame and the target prediction image and the motion estimation vector.

Image encoding, decoding method and device, coder-decoder

An image processing method and apparatus, a device, a video processing method and a storage medium are provided. The image processing method includes: receiving an input image; and processing the input image by using the convolutional neural network to obtain an output image. A definition of the output image is higher than a definition of the input image. Processing the input image by using the convolutional neural network to obtain the output image includes: performing feature extraction on the input image; concatenating the input image and the plurality of first images; performing the feature extraction on the first image group; fusing the plurality of second images and the plurality of first images; concatenating the input image and the plurality of third images to obtain a second image group; and performing the feature extraction on the second image group to obtain the output image.

Image processing method and apparatus, device, and video processing method

An expression recognition method is described that includes acquiring a face image to be recognized, and inputting the face image into N different recognition models arranged in sequence for expression recognition and outputting an actual expression recognition result, the N different recognition models being configured to recognize different target expression types, wherein N is an integer greater than 1.

Expression recognition method and apparatus, electronic device, and storage medium

An image processing method and device, and a computer-readable storage medium are disclosed. The method includes: performing a channel expansion process on the input image to obtain a first intermediate image; performing a channel decomposition process for multiple times based on the first intermediate image, wherein each time of channel decomposition process includes: decomposing an image to be processed into a first decomposition image and a second decomposition image; concatenating first decomposition images generated in each time of channel decomposition process and second decomposition image generated in the last time of channel decomposition process to obtain a concatenated image; performing a post-processing process on the concatenated image to obtain a second intermediate image; and fusing the second intermediate image with the input image to obtain the first output image.

Image processing method and device, and computer-readable storage medium

The present disclosure provides an image processing method and an image processing apparatus. The image processing method includes: obtaining a to-be-converted SDR image; using a first convolutional network to perform feature analysis on the SDR image, to obtain N weights of the SDR image; where the N weights are respectively configured to characterize proportions of color information of the SDR image to color information characterized in preset N 3D lookup tables, the N 3D lookup tables are configured to characterize color information of different types; obtaining a first 3D lookup table for the SDR image according to the N weights and the N 3D lookup tables; using the first 3D lookup table to adjust the color information of the SDR image to obtain an HDR image; and using a second convolutional neural network to perform refinement correction on the HDR image to obtain an output image.

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Beijing, China

Guannan Chen