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

TL;DR

RT-Focuser is a lightweight, real-time image deblurring model designed for edge devices, achieving high performance with minimal parameters and fast inference suitable for applications like autonomous driving and medical imaging.

Contribution

The paper introduces RT-Focuser, a novel lightweight U-shaped network with specialized modules for efficient real-time image deblurring on edge devices.

Findings

Achieves 30.67 dB PSNR with only 5.85M parameters.

Runs at 140+ FPS on GPU and mobile devices.

Demonstrates strong potential for real-time edge deployment.

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.