Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Hanwen Liu

Pablo Navarrete Michelini

Dan Zhu

Lijie Zhang

Guannan Chen

Yanhong Wu

Yunhua Lu

Ran Duan

Wenhao Zhang

Jingtao Xu

Tingting Wang

Xiaoyu Li

Wenbin Chen

Pablo Navarre Michelini

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

Disclosed are face clustering method and apparatus, image classification storage method, computer-readable storage medium and electronic device. The face clustering method includes: clustering to-be-clustered images, including: acquiring similarity threshold corresponding to quantity level of current image categories in image category library, at least two quantity levels corresponding to different similarity thresholds; judging, according to current similarity threshold and similarity between each to-be-clustered face image and at least one image category in image category library, whether there is image of same category as to-be-clustered face image in image category library; determining, when there is image of same category as to-be-clustered face image, category label of the to-be-clustered face image according to category label of image of same category as to-be-clustered face image; and assigning, when there is no image of same category as to-be-clustered face image, category label to the to-be-clustered face image according to first preset rule.

Face clustering method and apparatus, classification storage method, medium and electronic device

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 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

Provided are a training method and apparatus for an image processing image processing model, and an image processing method and apparatus. The training method comprises: acquiring a sample image and a first reference image, wherein the information quantity and resolution of the sample image are respectively lower than those of the first reference image; inputting the sample image into a generative network in an image processing model, and carrying out super-resolution processing and down-sampling processing on the sample image by means of the generative network, so as to generate and output at least one result image; determining the total image loss of the at least one result image according to the first reference image; and adjusting parameters of the generative network according to the total image loss, so that the total image loss of at least one result image output by the adjusted generative network meets an image loss condition.

Training method and apparatus for image processing model, and image processing method and apparatus

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

An image processing method, an image processing device, a training method of a neural network, an image processing method based on a combined neural network model, a constructing method of a combined neural network model, a neural network processor, and a storage medium are provided. The image processing method includes: obtaining, based on an input image, initial feature images of N stages with resolutions from high to low, where N is a positive integer and N>2, performing, based on initial feature images of second to N-th stages, cyclic scaling processing on an initial feature image of a first stage, to obtain an intermediate feature image; and preforming merging processing on the intermediate feature image to obtain an output image. The cyclic scaling processing includes hierarchically-nested scaling processing of N−1 stages, and scaling processing of each stage includes down-sampling processing, concatenating processing, up-sampling processing, and residual link addition processing.

Image processing method and device, training method of neural network, image processing method based on combined neural network model, constructing method of combined neural network model, neural network processor, and storage medium

A computer-implemented method is provided. The computer-implemented method includes rendering a dynamic effect to one or more objects in an image. The method includes setting boundary points surrounding a foreground object to define a boundary area in which a dynamic movement is to be realized; setting a movement line to define a dynamic movement direction along which the dynamic movement is to be realized, wherein setting the movement line includes detecting a continuous touch over different positions on the touch control display panel; setting a stationary area to define an area in which the dynamic movement is prohibited, wherein setting the stationary area includes detecting a first touch area corresponding to the stationary area on the touch control display panel; and generating an animation of the foreground object in the boundary area moving along the dynamic movement direction, thereby realizing the dynamic effect in the image.

Computer-implemented method of realizing dynamic effect in image, an apparatus for realizing dynamic effect in image, and computer-program product

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.

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

A computer-implemented method using a convolutional neural network is provided. The computer-implemented method using a convolutional neural network includes processing an input image through at least one channel of the convolutional neural network to generate an output image including content features of the input image morphed with style features of a reference style image. The at least one channel includes a down-sampling segment, a densely connected segment, and an up-sampling segment sequentially connected together. Processing the input image through the at least one channel of the convolutional neural network includes processing an input signal through the down-sampling segment to generate a down-sampling segment output; processing the down-sampling segment output through the densely connected segment to generate a densely connected segment output; and processing the densely connected segment output through the up-sampling segment to generate an up-sampling segment output. The input signal includes a component of the input image.

Computer-implemented method using a neural network comprising down-sampling segment, densely connected segment, and up-sampling segment, apparatus, and computer-program product

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

The present disclosure provides an image processing method, an image processing device, and a display device. The image processing method includes: acquiring image data including display data of all of pixels of an image; and performing, on the display data of each of the pixels, operations of: determining Euclidean distances from the display data of the pixel to the display data of a first pure color, the display data of a second pure color, and the display data of a third pure color in a set color space; and replacing the display data of the pixel with one of the display data of the first pure color, the display data of the second pure color and the display data of the third pure color according to the determined Euclidean distances and a preset rule.

Image processing method, image processing device, and display device

An embodiment of the present disclosure provides a method for processing an image. The method comprise: determining weights corresponding to candidate colors for a target color based on an original color of a pixel in the image; selecting a target color of the pixel from the candidate colors based on the weights; and converting the original color of the pixel into the target color to obtain a target image. According to the embodiment of the present disclosure, by determining the weights of the candidate colors, the image can be converted into the target image comprising only the candidate colors, thereby using a limited number of candidate colors to represent the target image.

Method for processing image storage medium and apparatus for the same

The present application discloses a color image processing method, a color image processing device, an electronic ink screen, and a storage medium. The color image processing method includes: obtaining an original image, and transforming original color data of a pixel in the original image into corresponding set color data in a set color space; determining a target color corresponding to the pixel in a plurality of set colors according to the set color data corresponding to the pixel and a color ratio allocation table; and generating a target image according to the target color.

Growing community of inventors

Beijing, China

Hanwen Liu