Motion blur caused by camera or object movement severely degrades image quality and poses challenges for real-time applications such as autonomous driving, UAV perception, and medical imaging. In this paper, a lightweight U-shaped network tailored for real-time deblurring is presented and named RT-Focuser. To balance speed and accuracy, we design three key components: Lightweight Deblurring Block (LD) for edge-aware feature extraction, Multi-Level Integrated Aggregation module (MLIA) for encoder integration, and Cross-source Fusion Block (X-Fuse) for progressive decoder refinement. Trained on a single blurred input, RT-Focuser achieves 30.67 dB PSNR with only 5.85M parameters and 15.76 GMACs. It runs 6ms per frame on GPU and mobile, exceeds 140 FPS on both, showing strong potential for deployment on the edge. The official code and usage are available on: https://github.com/ReaganWu/RT-Focuser.
@article{arxiv.2512.21975,
title = {RT-Focuser: A Real-Time Lightweight Model for Edge-side Image Deblurring},
author = {Zhuoyu Wu and Wenhui Ou and Qiawei Zheng and Jiayan Yang and Quanjun Wang and Wenqi Fang and Zheng Wang and Yongkui Yang and Heshan Li},
journal= {arXiv preprint arXiv:2512.21975},
year = {2025}
}
Comments
2 pages, 2 figures, this paper already accepted by IEEE ICTA 2025