Abstract: 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.

Abstract: An image processing device is disclosed. The image processing device 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 further configured to obtain a first image and a classifier that indicates a purpose of image processing, and to process, by using a deep neural network (DNN), the first image according to the purpose indicated by the classifier, wherein the DNN processes input images according to different purposes.

Abstract: A method of matching a voice for each object included in a video, includes: separating a plurality of voices in a video; determining a dissimilarity between the plurality of voices; selecting a partial duration in an entire duration of the video as a matching duration, based on the dissimilarity between the plurality of voices; matching, within the matching duration, the plurality of voices with a plurality of objects in the video respectively, based on mouth movements of the plurality of objects; and matching the plurality of voices with the plurality of objects respectively in the entire duration of the video, based on results of the matching between the plurality of voices and the plurality of objects within the matching duration.

Abstract: 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.

Abstract: 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.

Abstract: An electronic device for generating an image with an enhanced depth as perceived by a viewer includes at least one memory storing instructions, and at least one processor configured to execute the instructions to receive an input image, receive a control parameter for adjusting a first level of at least one pictorial depth cue included in the input image, and generate an output image including the at least one pictorial depth cue at a second level that is adjusted from the first level based on the control parameter using a deep neural network.

Abstract: Provided are methods and apparatus for encoding and decoding motion information. The method of encoding motion information includes: obtaining a motion information candidate by using motion information of prediction units that are temporally or spatially related to a current prediction unit; adding, when the number of motion information included in the motion information candidate is smaller than a predetermined number n, alternative motion information to the motion information candidate so that the number of motion information included in the motion information candidate reaches the predetermined number n; determining motion information with respect to the current prediction unit from among the n motion information candidates; and encoding index information indicating the determined motion information as motion information of the current prediction unit.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An image processing apparatus for performing an image by using one or more neural networks may include a memory storing one or more instructions and at least one processor configured to execute the one or more instructions to obtain classification information of a first image and first feature information of the first image, generate a first feature image for the first image by performing first image processing on the classification information and the first feature information, obtain second feature information by performing second image processing on the classification information and the first feature information, obtain fourth feature information by performing third image processing on third feature information extracted during the first image processing, generate a second feature image for the first image, based on the second feature information and the fourth feature information, and generate a second image based on the first feature image and the second feature image.

Abstract: An image processing apparatus for performing an image by using one or more neural networks may include a memory storing one or more instructions and at least one processor configured to execute the one or more instructions stored in the memory to obtain first feature information of a first image, generate an intermediate output image for the first image by performing first image processing on the first feature information, generate an attention map, based on the first image and the intermediate output image, obtain second feature information by performing second image processing on the first feature information, obtain fourth feature information by performing third image processing on third feature information extracted during the first image processing, and generate a second image having a higher quality than the first image, based on the attention map, the second feature information, and the fourth feature information.

Abstract: An image processing apparatus for processing an image by using one neural network, includes: a memory storing one instruction; and one processor configured to execute the one instruction to: obtain first feature data, based on a first image, obtain pieces of second feature data corresponding to first areas of the first image by performing first image processing on the first feature data, the first areas comprising a first number of pixels, obtain third feature data, based on the first image, obtain pieces of fourth feature data corresponding to second areas of the first image, by performing second image processing on the third feature data, the second areas comprising a second number of pixels that is greater than the first number, and generate a second image, based on the pieces of second feature data and the pieces of fourth feature data.

Abstract: An image processing apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: obtain similarity information, based on a pixel difference between each pixel included in a first image and an adjacent pixel of each pixel included in the first image; determine weight information based on the similarity information; obtain first feature information by performing a convolution operation between the first image and a first kernel; obtain second feature information by applying the weight information to the first feature information; and generate a second image based on the second feature information.

Abstract: Provided are methods and apparatus for encoding and decoding motion information. The method of encoding motion information includes: obtaining a motion information candidate by using motion information of prediction units that are temporally or spatially related to a current prediction unit; adding, when the number of motion information included in the motion information candidate is smaller than a predetermined number n, alternative motion information to the motion information candidate so that the number of motion information included in the motion information candidate reaches the predetermined number n; determining motion information with respect to the current prediction unit from among the n motion information candidates; and encoding index information indicating the determined motion information as motion information of the current prediction unit.

Abstract: An image processing apparatus for processing an image by using one or more convolutional neural networks includes a memory storing one or more instructions, and at least one processor configured to execute the one or more instructions stored in the memory to obtain first feature data by performing a convolution operation between input data obtained from a first image and a first kernel, divide a plurality of channels included in the first feature data into first groups, obtain second feature data by performing a convolution operation between the first feature data respectively corresponding to the first groups and second kernels respectively corresponding to the first groups, obtain shuffling data by shuffling the second feature data, obtain output data by performing a convolution operation between data obtained by summing channels included in the shuffling data and a third kernel, and generate a second image based on the output data.

Abstract: An image processing apparatus, including a processor configured to execute instructions stored in a memory to: obtain characteristic information of a first image, divide the characteristic information into a plurality of groups, input each group into a respective layer of a plurality of layers included in a convolutional neural network and perform a convolution operation using one or more kernels to obtain a plurality of pieces of output information, generate an attention map including weight information corresponding to each pixel included in the first image, based on the plurality of pieces of output information, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on the attention map and a spatial kernel including weight information according to a position relationship between the each pixel and a neighboring pixel, and generate a second image by applying the spatially variant kernel to the first image.

Abstract: Provided is a method of reducing noise of a compressed image, the method including obtaining a first image by applying a convolution filter, which sequentially performs down-convolution and up-convolution corresponding to the down-convolution, to a compressed image of an original image, obtaining a second image by subtracting the first image from the compressed image, obtaining noise comprising high-frequency information and a compressed artifact of the compressed image from the second image, obtaining a third image by removing the compressed artifact by applying a deep neural network (DNN) for removing the compressed artifact to the noise, and reconstructing the compressed image by summing the first image and the third image.

Abstract: An image processing apparatus includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction to use at least one neural network to: extract n pieces of first feature information from a first image, based on locations of pixels included in the first image, wherein n is a positive integer, generate n pieces of second feature information by performing a convolution operation between each of the n pieces of the first feature information and each of n kernels, and generate, based on the n pieces of the second feature information, a second image from which compression artifacts included in the first image are removed.