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公开(公告)号：US20220198616A1
公开(公告)日：2022-06-23
申请号：US17497824
申请日：2021-10-08
Inventor： Seung Yong LEE , Jun Yong LEE , Hyeong Seok SON , Sung Hyun CHO
Abstract: A video quality improvement method may comprise: inputting a structure feature map converted from current target frame by first convolution layer to first multi-task unit and second multi-task unit, which is connected to an output side of first multi-task unit, among the plurality of multi-task units; inputting a main input obtained by adding the structure feature map to a feature space, which is converted by second convolution layer from those obtained by concatenating, in channel dimension, a previous target frame and a correction frame of the previous frame to first multi-task unit; and inputting current target frame to Nth multi-task unit connected to an end of output side of second multi-task unit, wherein Nth multi-task unit outputs a correction frame of current target frame, and machine learning of the video quality improvement model is performed using an objective function calculated through the correction frame of current target frame.
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公开(公告)号：US20230196520A1
公开(公告)日：2023-06-22
申请号：US17974383
申请日：2022-10-26
Inventor： Seung Yong LEE , Hyeong Seok SON , Sung Hyun CHO , Jun Yong LEE
Abstract: The present disclosure provides a method of effectively deblurring a defocus blur in an input image based on an inverse kernel. The defocus deblurring method includes: generating, by an encoder network, an input feature map by encoding the input image; filtering, by an atrous convolution network including a plurality of atrous convolutional layers arranged in parallel, the input feature map to generate an output feature map having reduced blur component; and generating, by a decoder network, an output image having reduced blur from the output feature map with the reduced blur component and the input image.
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公开(公告)号：US20220366539A1
公开(公告)日：2022-11-17
申请号：US17770993
申请日：2020-11-11
Inventor： Seung Yong LEE , Sung Hyun CHO , Hyeong Seok SON
Abstract: An image processing method and apparatus based on machine learning are disclosed. The image processing method based on machine learning, according to the present invention, may comprise the steps of: generating a first corrected image by inputting an input image to a first convolution neural network; generating an intermediate image on the basis of the input image; performing machine learning on a first loss function of the first convolution neural network on the basis of the first corrected image and the intermediate image; and performing machine learning on a second loss function of the first convolution neural network on the basis of the first corrected image and a natural image.
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4.发明公开公开(公告)号：US20240212093A1
公开(公告)日：2024-06-27
申请号：US18396411
申请日：2023-12-26
Inventor： Seung Yong LEE , Sung Hyun CHO , Jun Yong LEE
IPC: G06T3/4053 , G06T3/4046
CPC classification number: G06T3/4053 , G06T3/4046
Abstract: A method for generating a super-resolution video by using a multi-camera video may comprise: generating a resolution-improved ultra-wide-angle video frame at an arbitrary time step by inputting an ultra-wide-angle video frame of a first resolution at the arbitrary time step, ultra-wide-angle video frames right before and right after the arbitrary time step, and a wide-angle video frame for reference at the arbitrary time step, to a bidirectional neural network, wherein the generating of the resolution-improved ultra-wide-angle video frame is performed using accumulated information at a past time step based on the arbitrary time step, and accumulated information at a future time step based on the arbitrary time step, and wherein a second resolution, which is a resolution of the generated ultra-wide-angle video frame, is greater than the first resolution.
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