Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Yunhua Lu

Pablo Navarrete Michelini

Hanwen Liu

Lijie Zhang

Ran Duan

Guannan Chen

Dan Zhu

An image processing method, comprising: by using a trained multi-scale detail enhancement model, performing detail enhancement on an input image to be processed; wherein multi-scale decomposition is performed on the input image to obtain a base layer image and at least one detail layer image; a first residual feature corresponding to a first feature map is acquired, and a second residual feature corresponding to a second feature map of each detail layer image is acquired; a base layer output image is obtained according to the first residual feature, each second residual feature and the first feature map, and a detail layer output image corresponding to the detail layer image is obtained according to the first residual feature, each second residual feature and the second feature map; and image fusion is performed on the base layer output image and each detail layer output image to obtain an output image.

Multi-scale detail enhancement model for images

The present disclosure provides a video processing method, a video processing device, an electronic apparatus, and a readable storage medium. The video processing method includes: obtaining input data; and inputting the input data into a video processing model to obtain output video data. A resolution and/or a duration of the output video data is not equal to a resolution and/or a duration of the input data, the video processing model includes a plurality of generators arranged in sequence and corresponding to different image resolutions, each generator includes a transposed 3D convolution unit and a plurality of first 3D convolutional layers, the transposed 3D convolution unit is configured to generate first output data in accordance with the input data and intermediate processing data of the generator.

Video processing method and device, electronic apparatus, and readable storage medium

An undecimated image processing method includes: acquiring an image to be processed, inputting the image to be processed into an image processing network, to obtain an output image, where a resolution of the output image is the same with a resolution of the image to be processed. The inputting the image to be processed into the image processing network to obtain the output image includes: inputting the image to be processed into an analysis module to perform feature analysis, and outputting a feature tensor image; inputting the feature tensor image into a processing module, and outputting a processed feature tensor image; and synthesizing, by a synthesis module, at least one feature tensor image outputted by the at least one processing module to obtain the output image.

Undecimated image processing method and device

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.

Image processing method and device, and training method of image processing model and training method thereof

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 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.

Method, device and computer readable storage medium for video frame interpolation

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.

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Beijing, China

Yunhua Lu