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公开(公告)号：US20250131537A1
公开(公告)日：2025-04-24
申请号：US18920493
申请日：2024-10-18
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Hyunseung LEE , Donghyun Kim , Kyuha Choi , Youngsu Moon , Younghoon Jeong
Abstract: Provided are an image processing device and an operating method of the same. The image processing device includes a memory storing one or more instructions, and at least one processor processing circuitry, and memory storing one or more instructions that, when executed by the at least one processor individually or collectively, cause the image processing device to obtain a neural network model corresponding to a quality of an input image and viewing information related to the input image. The at least one processor is configured to generate training data, based on the quality of the input image and the viewing information. The at least one processor is configured to train the neural network model by using the training data. The at least one processor is configured to obtain an image quality processed output image from the input image, based on the trained neural network model.
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公开(公告)号：US20240428403A1
公开(公告)日：2024-12-26
申请号：US18633085
申请日：2024-04-11
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jaesung Park , Youngsu Moon , Younghoon Jeong
Abstract: A display device includes: a display; communication circuitry configured to communicate with an external device; and at least one memory storing one or more instructions; and at least one processor configured to execute the one or more instructions, wherein the one or more instructions, when executed by the at least one processor, cause the display device to: display an image on the display based on image information received through the communication circuitry, identify, in the image, one or more feature points of a patient and an omega shape of the patient, identify, based on the one or more feature points and the omega shape, whether the image may include a predetermined detection area associated with biometric information of the patient, and based on identifying that the image may include the predetermined detection area, adjust a display parameter of at least one of the display or the image.
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公开(公告)号：US11295412B2
公开(公告)日：2022-04-05
申请号：US16838650
申请日：2020-04-02
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Cheon Lee , Donghyun Kim , Yongsup Park , Jaeyeon Park , Iljun Ahn , Hyunseung Lee , Taegyoung Ahn , Youngsu Moon , Tammy Lee
Abstract: An image processing apparatus applies an image to a first learning network model to optimize the edges of the image, applies the image to a second learning network model to optimize the texture of the image, and applies a first weight to the first image and a second weight to the second image based on information on the edge areas and the texture areas of the image to acquire an output image.
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公开(公告)号：US11263789B2
公开(公告)日：2022-03-01
申请号：US16399018
申请日：2019-04-30
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Hyungjun Lim , Taegyoung Ahn , Youngsu Moon
Abstract: Disclosed is an image processing apparatus and a method of operating the same. The image processing apparatus includes: a memory storing information on at least one random patch; and at least one processor configured to: obtain correlations between a pixel block included in an input image and each of a plurality of random patches obtained from the information on the at least one random patch, obtain weights respectively for the plurality of random patches on a basis of the obtained correlations and apply the weights respectively to the plurality of random patches, and obtain an output image by applying, to the pixel block, the plurality of random patches to which the weights are respectively applied.
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公开(公告)号：US11257189B2
公开(公告)日：2022-02-22
申请号：US16734001
申请日：2020-01-03
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Seungho Park , Youngsu Moon
Abstract: An electronic apparatus is disclosed. The electronic apparatus includes a memory storing at least one instruction, and a processor, electrically connected to the memory, configured to, by executing the instruction, obtain, from an input image, a noise map corresponding to the input image; provide the input image to an input layer of a learning network model including a plurality of layers, the learning network model being an artificial intelligence (AI) model that is obtained by learning, through an AI algorithm, a relationship between a plurality of sample images, a respective noise map of each of the plurality of sample images, and an original image corresponding to the plurality of sample images; provide the noise map to at least one intermediate layer among the plurality of layers; and obtain an output image based on a result from providing the input image and the noise map to the learning network model.
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Patent Agency Ranking
公开(公告)号：US12100118B2
公开(公告)日：2024-09-24
申请号：US17221105
申请日：2021-04-02
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Hyunseung Lee , Donghyun Kim , Youngsu Moon , Seungho Park , Younghoon Jeong
IPC: G06T3/4046 , G06F18/21 , G06N3/045 , G06N3/08
CPC classification number: G06T3/4046 , G06F18/2163 , G06N3/045 , G06N3/08
Abstract: An electronic apparatus is disclosed. The electronic apparatus includes a memory configured to store a plurality of neural network models, and a processor connected to the memory and control the electronic apparatus in which the processor is configured to obtain a weight map based on an object area included in an input image, and obtain a plurality of images by inputting the input image to each of the plurality of neural network models, and obtain an output image by weighting the plurality of images based on the weight map, and each of the plurality of neural network models is a model trained to upscale an image.
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公开(公告)号：US11551332B2
公开(公告)日：2023-01-10
申请号：US16908021
申请日：2020-06-22
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Donghyun Kim , Jisu Lee , Youngsu Moon , Seungho Park , Taegyoung Ahn , Younghoon Jeong
Abstract: Disclosed is an electronic apparatus. The electronic apparatus includes a processor configured to obtain first upscaling information of an input image using an artificial intelligence (AI) model that is trained to obtain upscaling information of an image. The processor is also configured to downscale the input image based on the obtained first upscaling information, and obtain an output image by upscaling the downscaled image based on an output resolution.
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公开(公告)号：US11443461B2
公开(公告)日：2022-09-13
申请号：US16399018
申请日：2019-04-30
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Hyungjun Lim , Taegyoung Ahn , Youngsu Moon
Abstract: Disclosed is an image processing apparatus and a method of operating the same. The image processing apparatus includes: a memory storing information on at least one random patch; and at least one processor configured to: obtain correlations between a pixel block included in an input image and each of a plurality of random patches obtained from the information on the at least one random patch, obtain weights respectively for the plurality of random patches on a basis of the obtained correlations and apply the weights respectively to the plurality of random patches, and obtain an output image by applying, to the pixel block, the plurality of random patches to which the weights are respectively applied.
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公开(公告)号：US11153575B2
公开(公告)日：2021-10-19
申请号：US16292655
申请日：2019-03-05
Inventor： Hyunseung Lee , Donghyun Kim , Youngsu Moon , Taegyoung Ahn , Yoonsik Kim , Jaewoo Park , Jae Woong Soh , Nam Ik Cho , Byeongyong Ahn
IPC: H04N19/176 , G06K9/40 , G06N3/02 , H04N19/137
Abstract: An electronic apparatus is provided. The electronic apparatus includes a storage configured to store a compression rate network model configured to determine a compression rate applied to an image block from among a plurality of compression rates, and a plurality of compression noise removing network models configured to remove compression noise for each of the plurality of compression rates, and a processor configured to: obtain a compression rate of each of a plurality of image blocks included in a frame of a decoded moving picture based on the compression rate network model, obtain the compression rate of the frame based on the plurality of obtained compression rates, and remove compression noise of the frame based on a compression noise removing network model corresponding to the compression rate of the frame from among the plurality of compression noise removing network models. The compression rate network model can be obtained by learning image characteristics of a plurality of restored image blocks corresponding to each of the plurality of compression rates through a first artificial intelligence algorithm, and the plurality of restored image blocks can be generated by encoding a plurality of original image blocks, and decoding the encoded plurality of original image blocks, and the plurality of compression noise removing network models can be obtained by learning a relation between the plurality of original image blocks and the plurality of restored image blocks through a second artificial intelligence algorithm.
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公开(公告)号：US11861809B2
公开(公告)日：2024-01-02
申请号：US17565938
申请日：2021-12-30
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Seungho Park , Youngsu Moon
CPC classification number: G06T5/002 , G06T5/50 , G06T2207/20081 , G06T2207/20084 , G06T2207/20208
Abstract: An electronic apparatus is disclosed. The electronic apparatus includes a memory storing at least one instruction, and a processor, electrically connected to the memory, configured to, by executing the instruction, obtain, from an input image, a noise map corresponding to the input image; provide the input image to an input layer of a learning network model including a plurality of layers, the learning network model being an artificial intelligence (AI) model that is obtained by learning, through an AI algorithm, a relationship between a plurality of sample images, a respective noise map of each of the plurality of sample images, and an original image corresponding to the plurality of sample images; provide the noise map to at least one intermediate layer among the plurality of layers; and obtain an output image based on a result from providing the input image and the noise map to the learning network model.
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