Real-World Deep Local Motion Deblurring with Position Guidance

Previous deblurring methods mostly tackle global motion blur due to camera shake but struggle with local motion blur from object movement, facing challenges like the random motion blur locations, data imbalance, directional ambiguity, and positional uncertainty. To fill the vacancy of real-world local motion deblurring, we establish ReLoBlur, the first real-world local motion deblurring dataset. ReLoBlur is captured by a synchronized beam-splitting photographing system and annotated via our developed Local Blur Foreground Mask Generator (LBFMG). To bridge the gap between local and global motion deblurring, we propose a Local Blur-Aware Gated network (LBAG) with gate blocks to focus deblurring on blurred regions, and a Blur-Aware Patch Cropping Strategy (BAPC) to address the data imbalance problem. Acknowledging directional ambiguity and positional uncertainty from shooting errors and non-uniform object motion, we enhance LBAG with LBAGp, guided by center-related distance, and optimized by a symmetric minimization loss. Extensive experiments prove the reliability of the ReLoBlur dataset, and demonstrate that LBAG and LBAGp achieve better local motion deblurring performance compared to state-of-the-art (SOTA) CNN-based deblurring methods.