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公开(公告)号：US11842267B2
公开(公告)日：2023-12-12
申请号：US16492873
申请日：2019-03-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Lijie Zhang , Dan Zhu
IPC: G06N3/08 , G06T3/40 , G06F18/211 , G06F18/213
CPC classification number: G06N3/08 , G06F18/211 , G06F18/213 , G06T3/4007 , G06T3/4046
Abstract: 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.
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42.发明授权公开(公告)号：US11449751B2
公开(公告)日：2022-09-20
申请号：US16759669
申请日：2019-09-25
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Hanwen Liu , Dan Zhu , Pablo Navarrete Michelini
Abstract: The present disclosure provides a training method for generative adversarial network, which includes: extracting a first-resolution sample image from a second-resolution sample image; separately providing a first input image and a second input image for a generative network to generate a first output image and a second output image respectively, the first input image including a first-resolution sample image and a first noise image, the second input image including the first-resolution sample image and a second noise image; separately providing the first output image and a second-resolution sample image for a discriminative network to output a first discrimination result and a second discrimination result; and adjusting parameters of the generative network to reduce a loss function. The present disclosure further provides an image processing method using the generative adversarial network, a computer device, and a computer-readable storage medium.
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43.发明授权公开(公告)号：US11416746B2
公开(公告)日：2022-08-16
申请号：US16759669
申请日：2019-09-25
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Hanwen Liu , Dan Zhu , Pablo Navarrete Michelini
Abstract: The present disclosure provides a training method for generative adversarial network, which includes: extracting a first-resolution sample image from a second-resolution sample image; separately providing a first input image and a second input image for a generative network to generate a first output image and a second output image respectively, the first input image including a first-resolution sample image and a first noise image, the second input image including the first-resolution sample image and a second noise image; separately providing the first output image and a second-resolution sample image for a discriminative network to output a first discrimination result and a second discrimination result; and adjusting parameters of the generative network to reduce a loss function. The present disclosure further provides an image processing method using the generative adversarial network, a computer device, and a computer-readable storage medium.
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公开(公告)号：US11257187B2
公开(公告)日：2022-02-22
申请号：US16521808
申请日：2019-07-25
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Dan Zhu , Pablo Navarrete Michelini , Hanwen Liu
Abstract: An image processing method, an image processing device and a computer storage medium are provided. The method includes: performing a maximum-filtering processing on an original image and obtaining a luminance channel of the image; and performing a guided-filtering processing on the obtained luminance channel, and estimating and obtaining an incident component.
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公开(公告)号：US11227364B2
公开(公告)日：2022-01-18
申请号：US16613073
申请日：2019-05-17
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Pablo Navarrete Michelini , Lijie Zhang , Hanwen Liu
Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method includes processing an input image through the convolutional neural network to generate an output image including content features of the input image morphed with style features of a style image. The convolutional neural network includes a feature extraction sub-network, a morpher, and a decoder sub-network. Processing the input image through convolutional neural network includes extracting style features of the style image to generate a plurality of style feature maps using the feature extraction sub-network; extracting content features of the input image to generate a plurality of content feature maps using the feature extraction sub-network; morphing the plurality of content feature maps respectively with the plurality of style feature maps to generate a plurality of output feature maps using the morpher; and reconstructing the plurality of output feature maps through the decoder sub-network to generate the output image.
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Patent Agency Ranking
公开(公告)号：US20210365744A1
公开(公告)日：2021-11-25
申请号：US16614547
申请日：2019-06-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Dan Zhu , Hanwen Liu
Abstract: The present disclosure generally relates to the field of deep learning technologies. A cascaded system for classifying an image includes a first cascade layer including a first analysis module coupled to a first input terminal, and a first pooling module coupled to the first analysis module; a second cascade layer including a second analysis module coupled to a second input terminal, and a second pooling module coupled to the first pooling module and the second analysis module; a synthesis layer coupled to the second pooling module, and an activation layer coupled to the synthesis layer.
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公开(公告)号：US20210358082A1
公开(公告)日：2021-11-18
申请号：US16613073
申请日：2019-05-17
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Pablo Navarrete Michelini , Lijie Zhang , Hanwen Liu
Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method includes processing an input image through the convolutional neural network to generate an output image including content features of the input image morphed with style features of a style image. The convolutional neural network includes a feature extraction sub-network, a morpher, and a decoder sub-network. Processing the input image through convolutional neural network includes extracting style features of the style image to generate a plurality of style feature maps using the feature extraction sub-network; extracting content features of the input image to generate a plurality of content feature maps using the feature extraction sub-network; morphing the plurality of content feature maps respectively with the plurality of style feature maps to generate a plurality of output feature maps using the morpher; and reconstructing the plurality of output feature maps through the decoder sub-network to generate the output image.
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48.发明授权公开(公告)号：US11164059B2
公开(公告)日：2021-11-02
申请号：US16835809
申请日：2020-03-31
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Dan Zhu , Pablo Navarrete Michelini , Lijie Zhang , Hanwen Liu
Abstract: Disclosed is a two-dimensional code image generation method and apparatus, a storage medium and an electronic device related to the field of two-dimensional code image technology. The method includes obtaining an initial two-dimensional code image and a background image, and performing structured processing on the initial two-dimensional code image according to the background image to obtain a structured two-dimensional code image, performing mode transfer processing on the background image to obtain a background image of a target mode by a mode transfer model, and performing a fusion operation on the structured two-dimensional code image and the background image of the target mode to obtain a target two-dimensional code image.
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公开(公告)号：US11107194B2
公开(公告)日：2021-08-31
申请号：US16755044
申请日：2019-08-19
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Dan Zhu , Lijie Zhang
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 combing respective m′ number of input images to the respective one of the Mux units to obtain a respective combined image.
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公开(公告)号：US20210097649A1
公开(公告)日：2021-04-01
申请号：US16855063
申请日：2020-04-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Dan Zhu , Lijie Zhang
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.
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