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 electronic device and a control method for improving image quality include receiving a first-quality image as an input image; analyzing the input image to obtain image parameter information; detecting an object included in the input image to obtain object information; inputting the input image, the image parameter information, and the object information into a trained neural network model; obtaining a second-quality image having a higher image quality than a first-image quality; and outputting the second-quality image. The method may include generating an extended object map by combining an object map with the image parameter information, where the object map is obtained from the object information. Post-filtering techniques, including multi-band image filtering and applying pixel-wise gain values based on object information, may be performed on the output data from the neural network model to further enhance image quality.

Abstract: Provided is an electronic device and method of operating same, the electronic device including: memory storing instructions; and a processor configured to execute the instructions to: obtain an upscaled image by upscaling a plurality of pixels of an image; obtain channel groups by unshuffling pixels of the upscaled image; update the channel groups by subtracting a value based on a position of a pixel included in each of the channel groups in the upscaled image from a pixel value included in each of the channel groups; generate high-resolution channel groups by inputting the updated channel groups to a neural network; obtain a processed image having a resolution that is the same as a resolution of the upscaled image by shuffling the high-resolution channel groups; and obtain a final image in which the image is upscaled by performing convolution on the processed image with a preset filter.

Abstract: Disclosed is an image processing device and method including obtaining a first differential image having a resolution greater than a resolution of an input image and a parameter value for a tone curve by applying the input image to a neural network, obtaining a plurality of filtered images for different frequency bands by filtering the first differential image, and applying, to each filtered image among the plurality of filtered images, gain values corresponding to a sample value of an edge map of a first image upscaled from the input image.

Abstract: The present disclosure relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm, and applications therefore. Disclosed is an electronic device. The electronic device comprises: a storage unit which stores therein an artificial intelligence model trained to determine parameters for a plurality of filters used for image processing on the basis of a deep neural network (DNN); and a processor for determining, through the artificial intelligence mode, parameters for each of the plurality of filters used for image processing for an input image, and performing, through the plurality of filters, filtering of the input image on the basis of the determined parameters so as to perform image processing for the input image.

Abstract: An encoding apparatus connected to a learning circuit processing learning of a deep neural network and configured to perform encoding for reconfiguring connection or disconnection of a plurality of edges in a layer of the deep neural network using an edge sequence generated based on a random number sequence and dropout information indicating a ratio between connected edges and disconnected edges of a plurality of edges included in a layer of the deep neural network.

Abstract: Disclosed is an image processing method including obtaining, from a first image, object information of an important object included in the first image, obtaining control information for image quality processing, and obtaining a second image by performing image quality processing on the important object from the first image based on the object information and user control information.

Abstract: An encoding apparatus connected to a learning circuit processing learning of a deep neural network and configured to perform encoding for reconfiguring connection or disconnection of a plurality of edges in a layer of the deep neural network using an edge sequence generated based on a random number sequence and dropout information indicating a ratio between connected edges and disconnected edges of a plurality of edges included in a layer of the 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 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 encoding apparatus connected to a learning circuit processing learning of a deep neural network and configured to perform encoding for reconfiguring connection or disconnection of a plurality of edges in a layer of the deep neural network using an edge sequence generated based on a random number sequence and dropout information indicating a ratio between connected edges and disconnected edges of a plurality of edges included in a layer of the deep neural network.

Abstract: The disclosure relates to an artificial intelligence (AI) system that simulates functions such as cognition and judgment of the human brain by utilizing machine learning algorithms such as deep learning and its applications.

Abstract: An electronic device and a control method therefor are disclosed. A method for controlling an electronic device according to the present invention comprises the steps of: receiving a current frame; determining a region, within the current frame, where there is a movement, on the basis of a prior frame and the current frame; inputting the current frame into an artificial intelligence learning model on the basis of the region where there is the movement, to obtain information relating to at least one object included in the current frame; and determining the object included in the region where there is the movement, by using the obtained information relating to the at least one object. Therefore, electronic device can rapidly determine an object included in a frame configuring a captured image.

Abstract: An image processing apparatus obtains a first output image by applying an image to a first training network model, obtains a second output image by applying the image to a second training network model, and obtains a reconstructed image based on the first output image and the second output image. The first training network model is a model that uses a fixed parameter obtained through training of a plurality of sample images, the second training network model is trained to minimize a difference between a target image corresponding to the image and the reconstructed 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: 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 electronic device and a control method therefor are disclosed. A method for controlling an electronic device according to the present invention comprises the steps of: receiving a current frame; determining a region, within the current frame, where there is a movement, on the basis of a prior frame and the current frame; inputting the current frame into an artificial intelligence learning model on the basis of the region where there is the movement, to obtain information relating to at least one object included in the current frame; and determining the object included in the region where there is the movement, by using the obtained information relating to the at least one object. Therefore, electronic device can rapidly determine an object included in a frame configuring a captured image.

Abstract: The present disclosure relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm, and applications therefore. Disclosed is an electronic device. The electronic device comprises: a storage unit which stores therein an artificial intelligence model trained to determine parameters for a plurality of filters used for image processing on the basis of a deep neural network (DNN); and a processor for determining, through the artificial intelligence mode, parameters for each of the plurality of filters used for image processing for an input image, and performing, through the plurality of filters, filtering of the input image on the basis of the determined parameters so as to perform image processing for the input image.

Abstract: The disclosure relates to an artificial intelligence (AI) system that simulates functions such as cognition and judgment of the human brain by utilizing machine learning algorithms such as deep learning and its applications.

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.