Media monitoring method, apparatus and system

    公开(公告)号:US12058395B2

    公开(公告)日:2024-08-06

    申请号:US17761526

    申请日:2021-04-27

    CPC classification number: H04N21/2407 H04N21/23418 H04N21/812

    Abstract: 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.

    Image processing method and device, training method of neural network, and storage medium

    公开(公告)号:US11908102B2

    公开(公告)日:2024-02-20

    申请号:US17281291

    申请日:2020-05-28

    CPC classification number: G06T3/4046 G06N3/045 G06N3/08

    Abstract: Disclosed are an image processing method and device, a training method of a neural network and a storage medium. The image processing method includes: obtaining an input image, and processing the input image by using a generative network to generate an output image. The generate network includes a first sub-network and at least one second sub-network, and the processing the input image by using the generative network to generate the output image includes, processing the input image by using the first sub-network to obtain a plurality of first feature images; performing a branching process and a weight sharing process on the plurality of first feature images by using the at least one second sub-network to obtain a plurality of second feature images; and processing the plurality of second feature images to obtain the output image.

    Convolutional neural network processor, image processing method and electronic device

    公开(公告)号:US11216913B2

    公开(公告)日:2022-01-04

    申请号:US16855063

    申请日:2020-04-22

    Abstract: The present disclosure discloses a convolutional neural network processor, an image processing method and an electronic device. The method includes: receiving, by the first convolutional unit, the input image to be processed, extracting the N feature maps with different scales in the image to be processed, sending the N feature maps to the second convolutional unit, and sending the first feature map to the processing unit; fusing, by the processing unit, the received preset noise information and the first feature map, to obtain the second feature map, and sending the second feature map to the second convolutional unit; and fusing, by the second convolutional unit, the received N feature maps with the second feature map to obtain the processed image.

    NEURAL NETWORK FOR ENHANCING ORIGINAL IMAGE, AND COMPUTER-IMPLEMENTED METHOD FOR ENHANCING ORIGINAL IMAGE USING NEURAL NETWORK

    公开(公告)号:US20210233214A1

    公开(公告)日:2021-07-29

    申请号:US16755044

    申请日:2019-08-19

    Abstract: A neural network is provided. The neural network includes 2n number of sampling units sequentially connected; and a plurality of processing units. A respective one of the plurality of processing units is between two adjacent sampling units of the 2n number of sampling units. A first sampling unit to an n-th sample unit of the 2n number of sampling units are DeMux units. A respective one of the DeMux units is configured to rearrange pixels in a respective input image to the respective one of the DeMux units following a first scrambling rule to obtain a respective rearranged image. An (n+1)-th sample unit to a (2n)-th sample unit of the 2n number of sampling units are Mux units. A respective one of the Mux units is configured to combine respective m′ number of input images to the respective one of the Mux units to obtain a respective combined image.

    METHOD FOR RECONSTRUCTING HDR IMAGES, TERMINAL, AND ELECTRONIC DEVICE

    公开(公告)号:US20240338796A1

    公开(公告)日:2024-10-10

    申请号:US18681951

    申请日:2023-02-17

    Inventor: Dan Zhu Mengdi Sun

    Abstract: A method for reconstructing an HDR image includes: acquiring a plurality of original images with same photographing scene and different exposure degrees; screening out a reference image from the plurality of original images, performing feature alignment processing on remaining original images according to the reference image, and obtaining displacement images of the remaining original images; determining an enhanced image according to the reference image and the displacement images of the remaining original images, wherein the enhanced image is obtained by performing image enhancement processing on a fused image after a down-sampling operation, and the fused image is obtained by performing feature fusion on the reference image and the displacement images of the remaining original images; and reconstructing the HDR images corresponding to the plurality of original images according to the enhanced images.

    Video image de-interlacing method and video image de-interlacing device

    公开(公告)号:US11711491B2

    公开(公告)日:2023-07-25

    申请号:US17625714

    申请日:2021-03-02

    Inventor: Dan Zhu Ran Duan

    CPC classification number: H04N7/012 H04N7/0102

    Abstract: A video image de-interlacing method is provided. The method-includes: acquiring a single frame of original video image; extracting odd field data and even field data in an original video image; performing N-1 times of down-sampling on the odd field data to obtain N-1 odd field data with different resolutions and performing N-1 times of down-sampling on the even field data to obtain N-1 even field data with different resolutions; combining odd field data and even field data with the same resolution to obtain a down-sampled image; and inputting the original video image and the down-sampled image to the de-interlacing network for de-interlacing.

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