公开(公告)号：US12198302B2

公开(公告)日：2025-01-14

申请号：US17775340

申请日：2021-07-15

Applicant: BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Ran Duan ,  Hanwen Liu ,  Yunhua Lu

IPC: G06T15/00 ,  G06T3/40 ,  G06T5/50 ,  G06T7/194 ,  G06T11/60 ,  G06V10/46

Abstract: An image processing method includes: acquiring a first image containing a target object; inputting the first image into an image processing model to obtain a second image, the second image being a mask image of the target object in the first image, a value for each pixel in the second image being in a range of 0 to 1, inclusive ([0, 1]), and the range of 0 to 1, inclusive ([0, 1]) indicating a degree of relation between each pixel in the second image and a pixel in the target object; fusing the first image and a background image according to the second image to obtain a fused image; and providing a first interface and displaying the fused image on the first interface.

公开(公告)号：US12148200B2

公开(公告)日：2024-11-19

申请号：US17419726

申请日：2020-12-29

Applicant: BOE Technology Group Co., Ltd.

Inventor： Yunhua Lu ,  Hanwen Liu ,  Pablo Navarrete Michelini ,  Lijie Zhang ,  Dan Zhu

IPC: G06V10/77 ,  G06N3/045 ,  G06N3/08 ,  G06T3/4046 ,  G06V10/80 ,  G06V10/82

Abstract: 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.

公开(公告)号：US11800053B2

公开(公告)日：2023-10-24

申请号：US17278403

申请日：2020-05-29

Applicant: BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Yunhua Lu ,  Ran Duan ,  Guannan Chen ,  Lijie Zhang ,  Hanwen Liu

IPC: H04N7/01 ,  G06T7/50 ,  G06T7/246 ,  G06T7/269 ,  G06T3/40

CPC classification number: H04N7/0137 ,  G06T3/40 ,  G06T7/246 ,  G06T7/269 ,  G06T7/50 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: The present disclosure relates to the field of information display, and specifically to a method, device, computer readable storage medium, and electronic device for video frame interpolation. The method comprises: obtaining, based on two input frames, two initial optical flow maps corresponding to the two input frames; optimizing the initial optical flow maps to obtain target optical flow maps; obtaining an interpolation frame kernel, two depth maps and two context feature maps based on the two input frames; obtaining an output frame using a frame synthesis method based on the target optical flow maps, the depth maps, the context feature maps, and the interpolation frame kernel.

公开(公告)号：US11689693B2

公开(公告)日：2023-06-27

申请号：US17265568

申请日：2020-04-30

Applicant: BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Yunhua Lu ,  Guannan Chen ,  Ran Duan ,  Lijie Zhang ,  Hanwen Liu

IPC: H04N7/01 ,  G06N20/00

CPC classification number: H04N7/0135 ,  G06N20/00 ,  H04N7/0145

Abstract: A video frame interpolation method and device, and a computer-readable storage medium are described. The method includes: inputting at least two image frames into a video frame interpolation model to obtain at least one frame-interpolation image frame, training the initial model using a first loss to obtain a reference model, copying the reference model to obtain three reference models with shared parameters, selecting different target sample images according to a preset rules to train the first/second reference model to obtain a first/second frame-interpolation result; selecting third target sample images from the first/second frame-interpolation result to train the third reference model to obtain the frame-interpolation result, obtaining a total loss of the first training model based on the frame-interpolation result and the sample images, adjusting parameters of the first training model based on the total loss, and using a parameter model via a predetermined number of iterations as the video frame interpolation model.