Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.07708 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908444575924224 |
|---|---|
| author | Shang, Wei Ren, Dongwei Zhang, Wanying Zhu, Pengfei Hu, Qinghua Zuo, Wangmeng |
| author_facet | Shang, Wei Ren, Dongwei Zhang, Wanying Zhu, Pengfei Hu, Qinghua Zuo, Wangmeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring Shang, Wei Ren, Dongwei Zhang, Wanying Zhu, Pengfei Hu, Qinghua Zuo, Wangmeng Computer Vision and Pattern Recognition I.4.3 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. |
| title | Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring |
| topic | Computer Vision and Pattern Recognition I.4.3 |
| url | https://arxiv.org/abs/2507.07708 |