  Most existing deblurring methods focus on removing global blur caused by
camera shake, while they cannot well handle local blur caused by object
movements. To fill the vacancy of local deblurring in real scenes, we establish
the first real local motion blur dataset (ReLoBlur), which is captured by a
synchronized beam-splitting photographing system and corrected by a
post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware
Gated network (LBAG) and several local blur-aware techniques to bridge the gap
between global and local deblurring: 1) a blur detection approach based on
background subtraction to localize blurred regions; 2) a gate mechanism to
guide our network to focus on blurred regions; and 3) a blur-aware patch
cropping strategy to address data imbalance problem. Extensive experiments
prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves
better performance than state-of-the-art global deblurring methods without our
proposed local blur-aware techniques.
