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arXiv · 2512.21975

RT-Focuser: A Real-Time Lightweight Model for Edge-side Image Deblurring

Zhuoyu Wu·Wenhui Ou·Qiawei Zheng·Jiayan Yang·Quanjun Wang·Wenqi Fang·Zheng Wang·Yongkui Yang·Heshan Li
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Abstract

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.

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Zhuoyu Wu, Wenhui Ou, Qiawei Zheng, Jiayan Yang, Quanjun Wang, Wenqi Fang, Zheng Wang, Yongkui Yang, Heshan Li. 2025-12-26. RT-Focuser: A Real-Time Lightweight Model for Edge-side Image Deblurring. https://arxiv.org/abs/2512.21975

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