Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Iljun Ahn

Tammy Lee

Jaeyeon Park

Yongsup Park

Minsu Cheon

Hanul Shin

Soomin Kang

Jong Beom Ra

Kyoung-Yong Lee

Toshihiro Rifu

Seungeon Kim

Yongjin Chang

Youngchan Song

Donghyun Kim

Youngsu Moon

Byung-sun Choi

Cheon An Lee

Hyunseung Lee

JongHyon Yi

Taegyoung Ahn

Jaehee Kwak

Kwangpyo Choi

Jong-hyon Yi

Changhan Kim

Cheon Lee

Sangwook Baek

Heeseok Oh

Kiheum Cho

Jong-Hyon Yi

Provided are an image processing apparatus and an operation method of the image processing apparatus. The image processing apparatus includes a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory to, by using one or more convolution neural networks, extract target features by performing a convolution operation between features of target regions having same locations in a plurality of input images and a first kernel set, extract peripheral features by performing a convolution operation of features of peripheral regions located around the target regions in the plurality of input images and a second kernel set, and determine a feature of a region corresponding to the target regions in an output image, based on the target features and the peripheral features.

Image processing device for improving details of an image, and operation method of the same

An image processing apparatus, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to obtain similarity information indicating a similarity between each pixel of a plurality of pixels included in a first image and an adjacent pixel of the each pixel; generate a weight map including weight information corresponding to the each pixel, based on the similarity information; generate a spatially variant kernel including a plurality of kernels corresponding to the plurality of pixels, based on the weight map and a spatial kernel including weight information based on a location relationship between the each pixel and the adjacent pixel; and generate a second image by applying the spatially variant kernel to the first image.

Image processing apparatus and operation method thereof

An image processing apparatus may include: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor executes the one or more instructions to use one or more convolutional neural networks to: determine first parameters, based on position information of first pixels included in a first region of a first image and position information of samples of the first image; obtain sample values of the samples by applying the first parameters to pixel values of the first pixels; obtain feature information corresponding to the first region by performing a convolution operation between the sample values and a kernel of the one or more convolutional neural networks; and generate a second image, based on the feature information.

An image processing apparatus and a method of operating the same are provided. The method includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to obtain first frequency coefficient information by converting a first image into a frequency domain in units of blocks having a preset size, obtain correlation information indicating a correlation between at least one block of the first frequency coefficient information and a first kernel, generate a weight corresponding to the first frequency coefficient information based on the correlation information, generate second frequency coefficient information by rearranging coefficients included in the first frequency coefficient information, wherein the one or more of the coefficients having a same frequency is arranged into a same group, and obtain quality information of the first image based on the weight and the second frequency coefficient information.

Image processing apparatus and operating method of the same

An image processing apparatus, including a memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions to: based on a first image and a probability model, optimize an estimated pixel value and estimated gradient values of each pixel of an original image corresponding to the first image, obtain an estimated original image based on the optimized estimated pixel value of the each pixel of the original image, obtain a decontour map based on the optimized estimated pixel value and the estimated gradient values of the each pixel of the original image, and generate a second image by combining the first image with the estimated original image based on the decontour map.

Image processing apparatus and operating method thereof

An image processing device, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain kernel coefficient information corresponding to each pixel of a plurality of pixels included in a first image, using a convolution neural network including one or more convolution layers, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on a gradient kernel set including a plurality of gradient kernels corresponding to one or more gradient characteristics of the plurality of pixels, and the kernel coefficient information, and generate a second image, by applying the kernel included in the spatially variant kernel to a region centered on the each pixel, and filtering the first image.

Image processing device and operating method thereof

Provided is an image processing apparatus including a memory storing one or more instructions and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is further configured to execute the one or more instructions to generate a second image by performing a deconvolution operation on a first image and a kernel comprising one or more weights, set values of the one or more weights based on the second image, and adjust the values of the one or more weights based on positions of the one or more weights in the kernel.

Image processing apparatus performing a deconvolution operation and operation method thereof

Provided is an image processing apparatus for generating a high-resolution image. The image processing apparatus includes a memory storing one or more instructions and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to extract feature information regarding a low-resolution image of a current frame by using a first convolutional neural network, generate, based on the feature information, a first high-resolution image of the current frame, remove flickering of the first high-resolution image by using a high-resolution image of a previous frame, and remove flickering of a high-resolution image of a next frame by using at least one of a flickering-removed second high-resolution image of the current frame, or the feature information.

An electronic device is disclosed. The electronic device of the disclosure comprises: a memory in which a learned artificial intelligence model is stored; and a processor for inputting an input image to the artificial intelligence model and outputting an enlarged image with increased resolution, wherein the learned artificial intelligence model includes an upscaling module for acquiring the pixel values of interpolated pixels around a cell according to a function having a nonlinearly decreasing symmetric form with reference to an original pixel in the enlarged image, the original pixel corresponding to a pixel of the input image.

Electronic device, image processing method, and computer-readable recording medium

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

Provided is an image processing apparatus including a memory storing at least one instruction, and a processor configured to execute the at least one instruction stored in the memory to obtain first feature information by performing a convolution operation on a first image and a first kernel included in a first convolution layer among a plurality of convolution layers, obtain at least one piece of characteristic information, based on the first feature information; obtain second feature information, based on the first feature information and the at least one piece of characteristic information, obtain third feature information by performing a convolution operation on the obtained second feature information and a second kernel included in a second convolution layer that is a layer next to the first convolution layer among the plurality of convolution layers, and obtain an output image, based on the third feature information.

An image processing apparatus is disclosed. The present image processing apparatus comprises: a memory for storing a low dynamic range (LDR) image and a processor for adjusting the brightness of the LDR image by means of a pixel-specific brightness ratio identified using a first parameter, and acquiring a high dynamic range (HDR) image by adding or subtracting a pixel-specific correction value identified using a second parameter in the brightness-adjusted LDR image.

Image processing apparatus, image processing method, and computer-readable recording medium

A tomography apparatus includes a data acquirer configured to acquire partial images of an object including a first image and a second image having imaged a surface of a portion of the object corresponding to a first time and a second time, respectively, by performing a tomography scan on the object that is moving, and acquire first information indicating motion of the object by using the first image and the second image; and an image reconstructor configured to reconstruct a target image by using the first information.

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Daejeon, South Korea

Iljun Ahn