Abstract: An image processing apparatus is disclosed. The present image processing apparatus includes an input unit to which an image is input; and a processor which extracts visual characteristics by reducing an input image and obtains a high-definition image by reflecting extracted visual characteristics on the input image. The disclosure relates to an artificial intelligence (AI) system and application thereof that simulate functions such as cognition and decision-making of a human brain using a machine learning algorithm such as deep learning.

Abstract: Disclosed is an image processing apparatus. The present image processing apparatus comprises: an input unit for inputting an image; and a processor for shrinking the inputted image to a predetermined ratio, extracting a visual feature from the shrunken image, performing an image quality enhancement process reflecting the extracted visual feature in the inputted image, repeatedly performing, for a predetermined number of times, the shrinking, the extracting, and the image quality enhancement process on the image that has undergone the image quality enhancement process. The present disclosure relates to an artificial intelligence (AI) system and an application thereof that simulate the functions of a human brain, such as recognition, judgment, etc., by using a machine learning algorithm such as deep learning, etc.

Abstract: An image processing apparatus is provided. The image processing apparatus includes a processor configured to, in response to an image including a plurality of frames being input, change a predetermined parameter to a parameter corresponding to a compression rate of each of the plurality of frames for each frame, and process the input image by using the parameter changed for each frame, and an output interface configured to output the processed image.

Abstract: An image processing apparatus is provided. The image processing apparatus according to an exemplary embodiment includes a communicator configured to receive an image, and a processor configured to generate a first image obtained by performing image processing on the received image by using a parameter for image processing, generate a second image obtained by reducing the first image at a predetermined ratio, and extract respective visual features from the first image and the second image, wherein the processor is further configured to adjust the parameter to allow a difference between the visual feature of the first image and the visual feature of the second image to be within a predetermined range.

Abstract: Disclosed is an image processing apparatus. The present image processing apparatus comprises: an input unit for inputting an image; and a processor for shrinking the inputted image to a predetermined ratio, extracting a visual feature from the shrunken image, performing an image quality enhancement process reflecting the extracted visual feature in the inputted image, repeatedly performing, for a predetermined number of times, the shrinking, the extracting, and the image quality enhancement process on the image that has undergone the image quality enhancement process. The present disclosure relates to an artificial intelligence (AI) system and an application thereof that simulate the functions of a human brain, such as recognition, judgment, etc., by using a machine learning algorithm such as deep learning, etc.

Abstract: An image processing apparatus is disclosed. The present image processing apparatus includes an input unit to which an image is input; and a processor which extracts visual characteristics by reducing an input image and obtains a high-definition image by reflecting extracted visual characteristics on the input image. The disclosure relates to an artificial intelligence (AI) system and application thereof that simulate functions such as cognition and decision-making of a human brain using a machine learning algorithm such as deep learning.

Abstract: An image processing apparatus is provided. The image processing apparatus includes a processor configured to, in response to an image including a plurality of frames being input, change a predetermined parameter to a parameter corresponding to a compression rate of each of the plurality of frames for each frame, and process the input image by using the parameter changed for each frame, and an output interface configured to output the processed image.

Abstract: An image processing apparatus is provided. The image processing apparatus according to an exemplary embodiment includes a communicator configured to receive an image, and a processor configured to generate a first image obtained by performing image processing on the received image by using a parameter for image processing, generate a second image obtained by reducing the first image at a predetermined ratio, and extract respective visual features from the first image and the second image, wherein the processor is further configured to adjust the parameter to allow a difference between the visual feature of the first image and the visual feature of the second image to be within a predetermined range.

Abstract: The present invention relates to a method for motion compensation and state-of-motion specific attenuation correction of positron tomography images by using a small number of low-radiation-dose computer tomography images.

Abstract: An image-based super-resolution method using a cone-beam-based line-of-response (LOR) reconfiguration in a positron emission tomography (PET) image is provided. That is, an apparatus and method for reconfiguring a super-resolution PET image using a cone-beam-based LOR reconfiguration is provided.

Abstract: An image-based super-resolution method using a cone-beam-based line-of-response (LOR) reconfiguration in a positron emission tomography (PET) image is provided. That is, an apparatus and method for reconfiguring a super-resolution PET image using a cone-beam-based LOR reconfiguration is provided.

Abstract: An image-based super-resolution method using a cone-beam-based line-of-response (LOR) reconfiguration in a positron emission tomography (PET) image is provided. That is, an apparatus and method for reconfiguring a super-resolution PET image using a cone-beam-based LOR reconfiguration is provided.

Abstract: The present invention relates to a method for motion compensation, and state-of-motion specific attenuation correction of positron tomography images by using a small number of low-radiation-dose computer tomography images.

Abstract: An image-based super-resolution method using a cone-beam-based line-of-response (LOR) reconfiguration in a positron emission tomography (PET) image is provided. That is, an apparatus and method for reconfiguring a super-resolution PET image using a cone-beam-based LOR reconfiguration is provided.