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: 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: 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.

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 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 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: 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: 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 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.

Abstract: An apparatus for processing audio includes at least one processor configured to obtain a down-mixed audio signal from a bitstream, to obtain down-mixing-related information from the bitstream, to de-mix the down-mixing-related information by using down-mixing-related information, and to reconstruct an audio signal including at least one frame based on the de-mixed audio signal. The down-mixing-related information is information generated in units of frames by using an audio scene type.

Abstract: 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.

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: Provided are a method of generating a partial area of an image by using a generative model and an electronic device for performing the method. The method of generating a partial area of an image by using a generative model includes obtaining an image comprising information of the partial area, obtaining an intermediate generated image by inputting the image into a first generative model, the intermediate generated image comprising first image information corresponding to the partial area, and obtaining a final generated image comprising second image information by inputting the image and the intermediate generated image to a second generative model, the second image information being at least partially different from the first image information.

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: Disclosed are an audio processing apparatus and method including obtaining at least one substream and additional information by parsing a bitstream, obtaining at least one audio signal of at least one channel group (CG) by decompressing the at least one substream, and obtaining a multi-channel audio signal by de-mixing the at least one audio signal of the at least one CG, based on the additional information. The additional information includes a weight index offset identified based on an energy value of a height channel of the multi-channel audio signal and an energy value of a surround channel of the multi-channel audio signal.

Abstract: A method of editing an image by using a generative model includes receiving a user input for a movement of at least one object included in an input image; expanding the input image in a direction based on the movement of the at least one object; determining a generation required area based on the expanded input image, wherein the generation required area is an area in which generation of a partial image for the at least one object is required; generating the partial image for the at least one object in the generation required area by using at least one generative model; and outputting a recomposed image based on the input image and the partial image for the at least one object.

Abstract: According to various embodiments of the disclosure, an audio processing apparatus includes at least one processor configured to execute one or more instructions to obtain a second audio signal down-mixed from at least one first audio signal, obtain information related to error removal for the at least one first audio signal, de-mix the at least one first audio signal from the down-mixed second audio signal, and reconstruct the at least one first audio signal by applying the information related to the error removal for the at least one first audio signal to the at least one first audio signal de-mixed from the second audio signal. The information related to the error removal having been generated using at least one of an original signal power of the at least one first audio signal or a second signal power of the at least one first audio signal after decoding.

Abstract: 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.

Abstract: 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.

Abstract: 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.