Skhynix Inc.

Jong Hyun Bae

Jin Su Kim

Tae Hyun Kim

Sung Joo Hong

Sang Woo Ahn

A device for correcting artifacts included in images includes an image processor. The image processor includes an acquirer configured to acquire image data including first pixel data, second pixel data, third pixel data, and fourth pixel data arranged in a 2×2 array. The image processor also includes a guide image generator configured to acquire a first guide image based on an average value of the first pixel data and the fourth pixel data that are located in a diagonal direction. The image processor further includes an image operating component configured to output a result image generated using a high-frequency component, included in the first guide image, and the image data.

Device for correcting artifacts included in images

An electronic apparatus includes: an image sensor for acquiring an image including normal pixel values that are sensed during a first exposure time and short exposure pixel values that are sensed during a second exposure time that is shorter than the first exposure time; and a processor configured to acquire flag information that indicates that a selected region, among a plurality of regions of the image, is one of a motion region and a normal region by using an exposure ratio of the first exposure time and the second exposure time and by using a short exposure pixel value and normal pixel values, which are included in the selected region, and configured to output a restoration image including a restoration pixel value that is corrected from the short exposure pixel value that is included in the selected region, based on the flag information.

Electronic apparatus based on multiple exposure image and operating method thereof

An image processing system includes: an image signal processor including a first neural network, and processing an input image by using the first neural network so as to generate a post-processed image; and a discriminator including a second neural network, and receiving a target image and the post-processed image, and discriminating the target image and the post-processed image into a real image and a fake image by using the second neural network, wherein the second neural network is trained to discriminate the target image as a real image and to discriminate the post-processed image as a fake image, and the first neural network is trained in such a manner that the post-processed image is discriminated as a real image by the second neural network.

Image processing system

An image sensing device includes an analysis module suitable for analyzing, based on pixel values of a kernel, an image texture of the kernel including a target pixel group and one or more adjacent pixel groups, a sum module suitable for generating any one of a first target sum value and a second target sum value based on an analysis result of the analysis module, wherein first target sum value is obtained by applying texture characteristics of the kernel in target pixel values of the target pixel group and the second target sum value is obtained without applying the texture characteristics of the kernel in the target pixel values, and a processing module suitable for generating a sum image based on any one of the first and second target sum values.

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Gyeonggi-do, South Korea

Jong Hyun Bae