Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Wei Shang, Dongwei Ren, Wanying Zhang, Pengfei Zhu, Qinghua Hu, Wangmeng Zuo

TL;DR
This paper introduces a novel, efficient local motion deblurring method that adaptively identifies blurred regions and reduces computational costs through pixel pruning and structural reparameterization, achieving superior results.
Contribution
The paper presents a trainable mask predictor and intra-frame motion analyzer for adaptive, efficient local motion deblurring with end-to-end training and significant FLOPs reduction.
Findings
Outperforms state-of-the-art methods on local and global blur datasets.
Reduces FLOPs by 49% compared to leading models.
Achieves superior deblurring quality with efficient computation.
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 convolutions to computationally efficient 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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Video Quality Assessment · Generative Adversarial Networks and Image Synthesis
