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Deep Hybrid Camera Deblurring for Smartphone Cameras
Jaesung Rim, Junyong Lee, Heemin Yang, Sunghyun Cho
SIGGRAPH, 2024
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We proposed a novel deblurring framework that simultaneously utilizes wide and ultra-wide cameras on smartphones. For training and evaluation, we collected the HCBlur dataset using a real smartphone.
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Burst Image Super-Resolution with Base Frame Selection
Sanghyun Kim*, Min Jung Lee*, Woohyeok Kim, Deunsol Jung, Jaesung Rim, Sunghyun Cho, Minsu Cho (*equal contribution)
NTIRE CVPRW, 2024
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ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images
Dongwoo Lee, Jeongtaek Oh, Jaesung Rim, Sunghyun Cho, Kyoung Mu Lee
ICCV, 2023
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Human Pose Estimation in Extremely Low-Light Conditions
Sohyun Lee*, Jaesung Rim*, Boseung Jeong, Geonu Kim, ByungJu Woo, Haechan Lee, Sunghyun Cho, Suha Kwak (*equal contribution)
CVPR, 2023
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We proposed the ExLPose dataset, which provides pairs of well-lit and low-light images along with their pose labels. We presented a novel method utilizing the knowledge from both images.
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Realistic Blur Synthesis for Learning Image Deblurring
Jaesung Rim, Geonung Kim, Jungeon Kim, Junyong Lee, Seungyong Lee, Sunghyun Cho
ECCV, 2022
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Deblurring networks trained on synthetic datasets often struggle with real-world images. To better handle real-world blurred images, we analyzed the differences between real and synthetic blur on the RSBlur dataset and proposed a realistic blur synthesis pipeline that includes a camera ISP simulation.
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Iterative Filter Adaptive Network for Single Image Defocus Deblurring
Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee
CVPR, 2021
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Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
Jaesung Rim, Haeyun Lee, Jucheol Won, Sunghyun Cho
ECCV, 2020
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We proposed the RealBlur dataset, which is the first real-world blur dataset for learning-based methods. This dataset is now utilized as a standard benchmark.
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Huawei Finland Research Center
2024/05/06 ~ 2024/08/31: Research Intern at Camera AI Solutions Team
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