Samsung patents image processor that adjusts filters based on pixel context

1. An image processing apparatus for processing an image by using one or more convolutional neural networks, the image processing apparatus comprising: 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: divide a plurality of channels included in input information obtained from a first image into first groups; obtain output data respectively corresponding to the first groups, based on input data respectively corresponding to the first groups by: obtaining first feature data based on a first convolution operation being performed between input data and a first kernel; dividing a plurality of channels included in the first feature data into second groups; obtaining second feature data based on a second convolution operation being performed between the first feature data respectively corresponding to the second groups and second kernels respectively corresponding to the second groups; obtaining shuffling data by shuffling the second feature data; and obtaining the output data by performing a convolution operation between data obtained by concatenating channels included in the shuffling data and a third kernel, obtain output information corresponding to the input information, by summing channels included in the output data respectively corresponding to the first groups; obtain third feature data based on a third convolution operation being performed between the output information and a fourth kernel; divide a plurality of channels included in the third feature data into the first groups; obtain fourth feature data based on a fourth convolution operation being performed between the third feature data respectively corresponding to the first groups and fifth kernels respectively corresponding to the first groups; divide a plurality of channels included in the fourth feature data into the second groups and obtain second shuffling data by shuffling the fourth feature data respectively corresponding to the second groups; obtain fifth feature data based on a fifth convolution operation being performed between the second shuffling data and sixth kernels respectively corresponding to the second groups; obtain sixth feature data respectively corresponding to the first groups by summing channels included in the fifth feature data; generate an attention map including weight information corresponding to each of a plurality of pixels included in the first image, based on the sixth feature data; generate a spatially variable kernel corresponding to each of the plurality of pixels, based on the attention map and a spatial kernel including weight information according to a position relationship between each of the plurality of pixels and at least one neighboring pixel of each of the plurality of pixels; and generate a second image by applying the spatially variable kernel to the first image.