Samsung Electronics Co., Ltd.

Sogang University Research Business Development Foundation

Korea Advanced Institute of Science and Technology

Seoul National University

Yonsei University

Ajou University Industry-academic Cooperation Foundation

Pukyong National University

Research & Industrial Cooperation Group

Youngsu Moon

Taegyoung Ahn

Donghyun Kim

Seungho Park

Hyunseung Lee

Hyungjun Lim

Tammy Lee

Mun Churl Kim

Younghoon Jeong

Cheon An Lee

Seung Joon Lee

Iljun Ahn

Jaeyeon Park

Yongsup Park

Jiyeun Kim

Younghun Jo

Siyeong Lee

Jisu Lee

Sung In Cho

Chul Ju Lee

Jaewoo Park

Yoonsik Choe

Wonseok Song

Jae Seok Choi

Joseph Kim

Sangkyun Kim

Sangsu Lee

Yongwoo Kim

Shinhaeng Kim

Yoonsik Kim

Nam Ik Cho

Gangmo Koo

MunChurl Kim

Cheon Lee

Byeongyong Ahn

Huijung Lee

Jaemoon Lim

Youngbin Kim

Suk-Ju Kang

Seungji Yang

Cheolkon Jung

Yongman Ro

Jae Woong Soh

Van Tu Vo

Suk Ju Kang

Gihwan Lee

Byungseok Min

Hoang Nam Nguyen

Wonchul Son

An electronic device is disclosed. The electronic device may comprise: a memory in which information on a first artificial intelligence model learned through first learning data and information on a second artificial intelligence model learned through the first learning data are stored; and a processor connected to the memory to control the electronic device, wherein the processor is configured to: input second learning data to each of the first artificial intelligence model and the second artificial intelligence model and relearns the second artificial intelligence model on the basis of an output of each of a plurality of first layers included in the first artificial intelligence model and an output of each of a plurality of second layers included in the second artificial intelligence model, each of the plurality of first layers includes a plurality of two-dimensional filters, and each of the plurality of second layers includes a plurality of filters obtained by reducing the size of each of the plurality of two-dimensional filters of a corresponding first layer.

Electronic device and control method thereof

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.

Electronic apparatus and controlling method thereof

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.

Applying random patches to pixel block of an image utilizing different weights

According to an embodiment of the disclosure, an electronic apparatus may include: a display configured to display an image; and a processor configured to: adjust, for each of sub areas having a specified size in an image quality degradation anticipation area of an image, a pixel value of at least one adjustment pixel among a plurality of pixels included in each sub area, and change the adjustment pixel into another pixel among the plurality of pixels, while maintaining a representative value of the plurality of pixels included in each sub area.

Electronic apparatus and control method thereof

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.

Electronic apparatus and image processing method thereof

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.

Image processing apparatus and image processing method thereof

Disclosed is an image processing device. The image processing device includes an input unit, and a processor which retargets an image frame input through the input unit and acquires an output image frame, wherein the processor identifies an area, in which pixel values are to be padded, on the basis of the input image frame and information about the output image frame, identifies a first pixel block on the basis of a target pixel area included in the identified area, identifies a second pixel block on the basis of pixel values included in the first pixel block, and pads the target pixel area on the basis of pixel values included in the second pixel block, so as to able to acquire the output image frame.

Image processing device and image processing method of same

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.

Display apparatus and image processing method for applying random patches to pixel block

Disclosed is an electronic device. The electronic device obtains a first histogram regarding a difference in gradation between adjacent pixels of an input image based on the first maximum output brightness, obtains a second histogram regarding a difference in gradation between the adjacent pixels of the input image based on the second maximum output brightness, obtains a third histogram regarding a difference in brightness between the adjacent pixels of the input image based on the first HVS recognition information, obtains a fourth histogram regarding a difference in brightness between the adjacent pixels of the input image based on the second HVS recognition information, and obtains a brightness value regarding the input image corresponding to the second maximum output brightness based on a difference between a first value obtained based on information on the first and third histograms and a second value obtained based on the second and fourth histograms.

An image processing apparatus obtains a first output image by applying an image to a first training network model, obtains a second output image by applying the image to a second training network model, and obtains a reconstructed image based on the first output image and the second output image. The first training network model is a model that uses a fixed parameter obtained through training of a plurality of sample images, the second training network model is trained to minimize a difference between a target image corresponding to the image and the reconstructed image.

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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Suwon-si, South Korea

Youngsu Moon