Samsung Electronics Co., Ltd.

Seoul National University

Taegyoung Ahn

Youngsu Moon

Donghyun Kim

Hyunseung Lee

Hyungjun Lim

Tammy Lee

Seungho Park

Iljun Ahn

Jaeyeon Park

Cheon An Lee

Yongsup Park

Younghoon Jeong

Jisu Lee

Jaewoo Park

Nam Ik Cho

Yoonsik Kim

Cheon Lee

Byeongyong Ahn

Jae Woong Soh

Soohong Kim

Bonggeun Lee

A method of an electronic device including obtaining a low-resolution input image by down-sampling a high-resolution input image; obtaining a low-resolution output image by performing image quality processing on the low-resolution input image; obtaining a low-resolution model from a conversion relationship between the low-resolution input image prior to the image quality processing being performed and the low-resolution output image subsequent to the image quality processing being performed; performing up-sampling of the low-resolution model; obtaining a high-resolution model by modifying the up-sampled low-resolution model, based on a difference between the high-resolution input image and the low-resolution input image; and obtaining a high-resolution output image from the high-resolution input image, by applying the high-resolution model to the high-resolution input image.

Electronic device and operation 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

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 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.

Electronic apparatus and controlling method thereof

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

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.

Electronic apparatus and control method thereof

An image processing apparatus and method are provided. The image processing apparatus includes: a memory configured to store at least one instruction, and a processor electrically connected to the memory, wherein the processor, by executing the at least one instruction, is configured to: apply an input image to a training network model; and apply, to a pixel block included in the input image, a texture patch corresponding to the pixel block to obtain an output image, wherein the training network model stores a plurality of texture patches corresponding to a plurality of classes classified based on a characteristic of an image, and is configured to train at least one texture patch, among the plurality of texture patches, based on the input image.

Display apparatus and image processing method thereof

Growing community of inventors

Suwon-si, South Korea

Taegyoung Ahn