Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

International Business Machines Corporation

Beijing Xiaomi Mobile Software Co., Ltd.

The Procter Gamble Company

Xiaomi Inc.

Purpana (beijing) Technologies Co., Ltd.

Beijing Xiaomi Mobile Software Co., Ltd. Nanjing Branch

Dan Zhu

Hanwen Liu

Pablo Navarrete Michelini

Lijie Zhang

Guannan Chen

Ran Duan

Yanhong Wu

Wei Sun

Dong Li

Wen Jun Yin

Miao Liu

Ying Shen

Xingchen Liu

Zhu Hong Cai

Kui Song

Wenhao Zhang

Jing Min Xu

Lin Liu

Feng Guo

Bingchuan Shi

Wenbin Chen

Zhijie Li

Yunhua Lu

Feng Hou

Daisong Tuo

Fukang Zhan

Xinyuan Xu

Liquan Sun

Chen Zou

Pablo Navarre Michelini

Jing Xu

An image restoration method and apparatus, and an electronic device. The method includes: inputting, into a target denoising network, an image to be processed, where the target denoising network includes a single-frame network and a recursive network, and the image to be processed is any frame in a video to be processed (S); removing, via the single-frame network, compression noise of the image to be processed, and outputting a first image (S); according to the content of the previous frame of image, removing, via the recursive network, compression noise of the image to be processed, and outputting a second image, where the previous frame of image is the previous frame of image of the image to be processed in the video to be processed (S); and performing weighted summation on the first image and the second image, and outputting a denoised image for the image to be processed (S).

Image restoration method and apparatus, and electronic device

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

Embodiments of the disclosure provide a method for detecting bad points in a video, including: performing extreme filtering respectively on first, second and third frames of images which are sequentially and continuously in the video to obtain first, second and third filtered images, respectively; wherein the extreme filtering is one of maximum filtering and minimum filtering; determining first and second difference images according to the first, second and third filtered images; determining a candidate image according to the first and second difference images; and determining that at least part of points in the second frame of image corresponding to the valid point in the candidate image are bad points. The embodiment of the disclosure also provides a device and a computer-readable medium for detecting bad points in the video.

Method and device for detecting bad points in video and computer readable 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

A logo labeling method and device, an update method and system, and a storage medium are provided. The logo labeling method includes: acquiring a standard logo picture including a logo; based on the standard logo picture, determining transparency channel information to obtain a first logo picture; determining a circumscribed rectangle frame of the logo in the first logo picture based on the transparency channel information, and performing cutting processing on the first logo picture according to the circumscribed rectangle frame to obtain a second logo picture; performing data augmentation processing based on the second logo picture to obtain at least one third logo picture; and acquiring a background picture, determining pasting position information of the third logo picture in the background picture, and pasting the third logo picture on the background picture according to the pasting position information to obtain at least one logo labeling picture.

Logo labeling method and device, update method and system of logo detection model, and storage medium

Provided are a method for enhancing a video resolution enhancement, a computer readable storage medium, and an electronic device. The method includes: obtaining multiple image frames as input data, and obtaining initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer; obtaining first feature data by performing down-sampling on the initial data at a preset multiple; obtaining first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data as one frame; and obtaining first output data by performing up-sampling on the first reference data at the preset multiple.

Video resolution enhancement method, storage medium, and electronic device

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

The present disclosure discloses a method, apparatus and system for customer group analysis, and a storage medium. The method includes: obtaining video images of a customer group passing by a display apparatus; recognizing and tracking head images of the customer group in the video images, and determining behavior characteristics of individuals in the customer group; and performing statistical analysis on the behavior characteristics of the customer group corresponding to the display apparatus, so as to update a quantity of individuals corresponding to each behavior characteristic.

Method, apparatus and system for customer group analysis, and storage medium

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

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 discloses a user interface system, electronic equipment and an interaction method for picture recognition. The electronic equipment includes a display screen, a memory and a processor, a first interface is displayed on the display screen, and the first interface includes at least one primary function classification tag, a plurality of secondary function classification tags and at least one tertiary function classification tag included in each of the secondary function classification tags; and by selecting a tertiary function classification tag, a second interface can be displayed such that a function effect corresponding to the tertiary function classification tag is experienced on the second interface. The electronic equipment can be used such that different tertiary function classification tags can be selected for experience in the first interface, can provide users with practicality, and has certain tool properties.

User interface system, electronic equipment and interaction method for picture recognition

A method for processing an image includes acquiring an input image, performing down-sampling and feature extraction on the input image by an encoder network to obtain multiple feature maps, and performing up-sampling and feature extraction on the multiple feature maps by a decoder network to obtain a target segmentation image. Processing levels between the encoder network and the decoder network for outputting feature maps with the same resolution are connected with each other. The encoder network and the decoder network each includes one or more dense calculation blocks, and at least one convolution module in any dense computation block includes at least one group of asymmetric convolution kernels.

Image processing method, image processing apparatus, and device

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.

Image processing method and image processing apparatus

The present disclosure discloses a media monitoring method, apparatus and system. The method includes: obtaining image information of a program verification region in a display frame of a currently monitored display screen, wherein the program verification region is configured to display verification information corresponding to a current program that should be broadcast on the display screen; identifying the image information to determine actual display information representing a current actually-broadcast program; and performing verification on the actual display information according to the verification information, and determining that the display screen broadcasts normally if the verification is passed, or determining that the display screen does not broadcast normally if the verification is failed.

Growing community of inventors

Beijing, China

Dan Zhu