Abstract:
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.
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 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 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 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:
A tomography apparatus includes a data acquirer which acquires a first image which corresponds to a first time point and a second image which corresponds to a second time point by performing a tomography scan on an object; an image reconstructor which acquires first information which relates to a relationship between a motion amount of the object and the time based on a motion amount between the first image and the second image, predicts a third image which corresponds to a third time point between the first and second time points based on the first information, corrects the first information by using the predicted third image and measured data which corresponds to the third time point, and reconstructs the third image by using the corrected first information; and a display which displays the reconstructed third 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 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, 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 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.