8 papers
Realistic Compound-Lens Defocus Blur Synthesis
Yunkyu Lee, Woohyeok Kim, Sunghyun Cho
Defocus blur degrades fine image structures and limits visual perception, which can adversely affect downstream vision tasks. Although recent deep learning deblurring methods have…
POS-ISP: Pipeline Optimization at the Sequence Level for Task-aware ISP
Jiyun Won, Heemin Yang, Woohyeok Kim +2
Recent work has explored optimizing image signal processing (ISP) pipelines for various tasks by composing predefined modules and adapting them to task-specific objectives. However…
Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde +4
This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a…
Gyro-based Neural Single Image Deblurring
Heemin Yang, Jaesung Rim, Seungyong Lee +2
In this paper, we present GyroDeblurNet, a novel single-image deblurring method that utilizes a gyro sensor to resolve the ill-posedness of image deblurring. The gyro sensor provid…
Exploiting Deblurring Networks for Radiance Fields
Haeyun Choi, Heemin Yang, Janghyeok Han +1
In this paper, we propose DeepDeblurRF, a novel radiance field deblurring approach that can synthesize high-quality novel views from blurred training views with significantly reduc…
Deep Hybrid Camera Deblurring for Smartphone Cameras
Jaesung Rim, Junyong Lee, Heemin Yang +1
Mobile cameras, despite their significant advancements, still have difficulty in low-light imaging due to compact sensors and lenses, leading to longer exposures and motion blur. T…