Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Local motion blur in digital images originates from the relative motion
between dynamic objects and static imaging systems during exposure. Existing
deblurring methods face significant challenges in addressing this problem due
to their inefficient allocation of computational resources and inadequate
handling of spatially varying blur patterns. To overcome these limitations, we
first propose a trainable mask predictor that identifies blurred regions in the
image. During training, we employ blur masks to exclude sharp regions. For
inference optimization, we implement structural reparameterization by
converting $3\times 3$ convolutions to computationally efficient $1\times 1$
convolutions, enabling pixel-level pruning of sharp areas to reduce
computation. Second, we develop an intra-frame motion analyzer that translates
relative pixel displacements into motion trajectories, establishing adaptive
guidance for region-specific blur restoration. Our method is trained end-to-end
using a combination of reconstruction loss, reblur loss, and mask loss guided
by annotated blur masks. Extensive experiments demonstrate superior performance
over state-of-the-art methods on both local and global blur datasets while
reducing FLOPs by 49\% compared to SOTA models (e.g., LMD-ViT). The source code
is available at https://github.com/shangwei5/M2AENet.
