Motion Deblur by Learning Residual From Events
Author:
Affiliation:
1. School of Electronic Information, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6046/10384483/10403964.pdf?arnumber=10403964
Reference51 articles.
1. Deep Image Deblurring: A Survey
2. CRNet: Unsupervised Color Retention Network for Blind Motion Deblurring
3. E-CIR: Event-Enhanced Continuous Intensity Recovery
4. FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. FE-DeTr: Keypoint Detection and Tracking in Low-quality Image Frames with Events;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. A novel dynamic scene deblurring framework based on hybrid activation and edge-assisted dual-branch residuals;The Visual Computer;2024-04-30
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