8 citations · 16 across the 10 of their papers we have counts for
10 papers · 1 filter
Learning to See Through with Events
Lei Yu, Xiang Zhang, Wei Liao +2
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi…
Learning to Extract Building Footprints from Off-Nadir Aerial Images
Jinwang Wang, Lingxuan Meng, Weijia Li +3
Extracting building footprints from aerial images is essential for precise urban mapping with photogrammetric computer vision technologies. Existing approaches mainly assume that t…
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Ziwei Luo, Youwei Li, Shen Cheng +6
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-…
Unifying Motion Deblurring and Frame Interpolation with Events
Xiang Zhang, Lei Yu
Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. T…
Autofocus for Event Cameras
Shijie Lin, Yinqiang Zhang, Lei Yu +3
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus se…
Motion Deblurring with Real Events
Fang Xu, Lei Yu, Bishan Wang +5
In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the per…