Samsung Electronics Co., Ltd.

Artem Sergeevich Migukin

Praveen Gulaka

Sang-cheon Choi

Dmitry Korobchenko

Sergey Stanislavovich Zavalishin

Alexey Bronislavovich Danilevich

Ilya Vasilievich Kurilin

Mikhail Yurievich Sirotenko

Ivan Olegovich Karacharov

Aleksei Mikhailovich Gruzdev

Yang Lim Choi

Vladimir Gennadievich Kurmanov

Vitaly Vladimirovich Chernov

Michail Nikolaevich Rychagov

Anna Andreevna Varfolomeeva

Alexey Stanislavovich Chernyavskiy

Dmitry Valerievich Polubotko

Kirill Arthurovich Gavrilyuk

Dmitry Alexandrovich Korobchenko

Sang-Cheon Choi

A method for super resolution of an image imitating optical zoom implemented on a resource-constrained mobile device is includes inputting a first-resolution image. The method also includes assigning a super resolution factor from a predefined set of super resolution factors that indicate increasing the first resolution to obtain a second-resolution image. The super resolution factor indicates a trained intelligent system imitating the operation of the corresponding optical system among a plurality of intelligent systems trained for each super resolution factor of the predefined set of super resolution factors. The method further includes transforming the first-resolution image into the second-resolution image using said trained intelligent system.

Method for image super resolution imitating optical zoom implemented on a resource-constrained mobile device, and a mobile device implementing the same

A method and an illumination manipulation device of performing an object illumination manipulation operation on a first image are provided. The method includes receiving the first image and an input of a light direction, generating a second image with a first object detected from the first image, determining a second object illumination of the second image based on the light direction and replacing a first object illumination of the first image with the second object illumination.

Method and an apparatus for performing object illumination manipulation on an image

Provided is a magnetic resonance imaging (MRI) apparatus including an acquisition unit configured to acquire an undersampled spectrum in a k-space and a reconstruction unit configured to generate a target image based on the undersampled spectrum, wherein the reconstruction unit includes: a first sub-reconstruction unit configured to perform initial reconstruction on data corresponding to unsampled positions in the k-space by using a Split Bregman algorithm or approximate sparse coding; a second sub-reconstruction unit configured to decompose the initially reconstructed spectrum in the k-space into multiple frequency bands to thereby generate a plurality of individual spectra and perform dictionary learning reconstruction on images respectively corresponding to the decomposed multiple frequency bands by alternating sparse approximation and reconstructing of measured frequencies; and an image generator configured to generate a target image by merging together the reconstructed images respectively corresponding to the multiple frequency bands.

Magnetic resonance imaging device and method for generating magnetic resonance image

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Moscow, Russia

Artem Sergeevich Migukin