Jaesung Rim

I am a Ph.D. student at POSTECH under the supervision of Prof. Sunghyun Cho. I received my Master's degree in ICE from DGIST. Previously, I did my bachelor's at Kwangwoon University.

My research interest includes computational photography, particularly image restoration, image deblurring, low-light imaging, and mobile imaging.

Email  /  GitHub  /  Google Scholar  /  CV  /  LinkedIn

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Publications

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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
Paper / Project / Code

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
Paper / Project / Code

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
Paper / Project / Code

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.

Work Experience

Huawei Finland Research Center


2024/05/06 ~ 2024/08/31: Research Intern at Camera AI Solutions Team

Design and source code from Jon Barron's website and Leonid Keselman's website