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Sung-Jin Cho

Department of Electrical Engineering, Korea University

6 papers hereh-index 9353 citations33 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • eess.IV1
affiliations
  • Department of Electrical Engineering, Korea University
same name
  • Sung-Jin Cho — 2 papers, h 3
  • Sung-Jin Cho — 1 paper, h 20
  • Sung-Jin Cho — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedW-Net: Two-stage U-Net with misaligned data for raw-to-RGB mapping

16 citations · 20 across the 6 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023

ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images

Dongwoo Lee, Jeongtaek Oh, Jaesung Rim +2

We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6…

cs.CV2023

Lightweight Monocular Depth Estimation via Token-Sharing Transformer

Dong-Jae Lee, Jae Young Lee, Hyounguk Shon +4

Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera ca…

cs.CV2023★ 2 cited

DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

Jiuhn Song, Seonghoon Park, Honggyu An +4

Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the numbe…

cs.CV2023

Human Pose Estimation in Extremely Low-Light Conditions

Sohyun Lee, Jaesung Rim, Boseung Jeong +5

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.