  Local motion blur commonly occurs in real-world photography due to the mixing
between moving objects and stationary backgrounds during exposure. Existing
image deblurring methods predominantly focus on global deblurring,
inadvertently affecting the sharpness of backgrounds in locally blurred images
and wasting unnecessary computation on sharp pixels, especially for
high-resolution images. This paper aims to adaptively and efficiently restore
high-resolution locally blurred images. We propose a local motion deblurring
vision Transformer (LMD-ViT) built on adaptive window pruning Transformer
blocks (AdaWPT). To focus deblurring on local regions and reduce computation,
AdaWPT prunes unnecessary windows, only allowing the active windows to be
involved in the deblurring processes. The pruning operation relies on the
blurriness confidence predicted by a confidence predictor that is trained
end-to-end using a reconstruction loss with Gumbel-Softmax re-parameterization
and a pruning loss guided by annotated blur masks. Our method removes local
motion blur effectively without distorting sharp regions, demonstrated by its
exceptional perceptual and quantitative improvements compared to
state-of-the-art methods. In addition, our approach substantially reduces FLOPs
by 66% and achieves more than a twofold increase in inference speed compared to
Transformer-based deblurring methods. We will make our code and annotated blur
masks publicly available.
