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公开(公告)号:EP4198878A1
公开(公告)日:2023-06-21
申请号:EP22189533.7
申请日:2022-08-09
发明人: CHOI, Jae Seok , CHO, Minsu , KIM, Sanghyun , LEE, Jongmin , KWON, Kinam , SEO, Geonseok , LEE, Hyong Euk
摘要: A method and apparatus for image restoration based on burst images. The method includes generating a plurality of feature representations corresponding to individual images of a burst image set by encoding the individual images (810), determining a reference feature representation from among the plurality of feature representations (820), determining a first comparison pair including the reference feature representation and a first feature representation of the plurality of feature representations (830), generating a first motion-embedding feature representation of the first comparison pair based on a similarity score map of the reference feature representation and the first feature representation (840), generating a fusion result by fusing a plurality of motion-embedding feature representations including the first motion-embedding feature representation (850), and generating at least one restored image by decoding the fusion result (860).
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公开(公告)号:EP4307211A1
公开(公告)日:2024-01-17
申请号:EP23171043.5
申请日:2023-05-02
发明人: YOO, Jaehyoung , KWON, Kinam , YU, Yunsang , LEE, Hyong Euk
IPC分类号: G06T5/00
摘要: An image restoration method and apparatus are provided. The image restoration method includes determining auxiliary data corresponding to a plurality of filter kernels by filtering target data with the plurality of filter kernels, determining new input data by combining the auxiliary data with at least some input data of layers of a neural network-based image restoration model, generating, based on the new input data, a restored image of the input image by executing the neural network-based image restoration model, wherein the filter kernels are not part of the neural network-based image restoration model.
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公开(公告)号:EP4181056A1
公开(公告)日:2023-05-17
申请号:EP22207450.2
申请日:2022-11-15
发明人: KWON, Kinam , KIM, Heewon , LEE, Kyoung Mu , LEE, Hyong Euk
IPC分类号: G06T5/00
摘要: The present disclosure concerns a processor-implemented method with image processing which includes providing retouch result candidates of an input image (101) to a user in response to applying vector value candidates to a style vector (103), determining a vector value of the style vector based on a selection of the user for the retouch result candidates, determining an adjustment parameter set (104) corresponding to the determined vector value of the style vector, and generating a retouch result (105) by adjusting the input image based on the adjustment parameter set.
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公开(公告)号:EP4163865A1
公开(公告)日:2023-04-12
申请号:EP22191990.5
申请日:2022-08-24
发明人: CHOI, Jae Seok , KWON, Kinam , LEE, Hyong Euk
IPC分类号: G06T5/00
摘要: A method and apparatus with noise consideration are provided. The method includes generating, using a noise model, a non-normal noise map corresponding to a noise of an input image, and generating an enhanced image of the input image by implementing an image enhancement model based on the input image and the non-normal noise map, where the noise of the input image follows a non-normal distribution.
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公开(公告)号:EP4033446A1
公开(公告)日:2022-07-27
申请号:EP21201300.7
申请日:2021-10-06
发明人: KWON, Kinam , KIM, Heewon , LEE, Kyoung Mu , LEE, Hyong Euk
摘要: A method with image restoration includes: receiving an input image and a first task vector indicating a first image effect among candidate image effects; extracting a common feature shared by the candidate image effects from the input image, based on a task-agnostic architecture of a source neural network; and restoring the common feature to a first restoration image corresponding to the first image effect, based on a task-specific architecture of the source neural network and the first task vector.
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公开(公告)号:EP3992904A1
公开(公告)日:2022-05-04
申请号:EP21185124.1
申请日:2021-07-12
发明人: KWON, Kinam , KANG, Eunhee , LEE, Sangwon , LEE, Sujin , LEE, Hyongeuk , CHOI, Jaeseok , YOO, Byungin
IPC分类号: G06T5/00
摘要: An image restoration method includes determining degradation information indicating a degradation factor of a degraded image, tuning the degradation information based on a tuning condition, and generating a restored image corresponding to the degraded image by executing an image restoration network with the degraded image and the degradation information.
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