Clear Sights on Site: A Spatial-Adaptive Channel Network for Deblurring Construction Site Images

The reliability of computer vision systems in the construction industry is critically undermined by motion blur, a complex and non-uniform degradation that conventional deblurring models with static architectures fail to address effectively. To overcome this challenge, we introduce the Spatial-Adaptive Channel Network (SAC-Net), a dynamic architecture designed specifically for this demanding environment. SAC-Net features three synergistic innovations. First, a Spatial-Adaptive Channel Module (SACM) generates content-aware spatial filters, allowing the network to adaptively focus on the most salient features for restoration. Second, our Hierarchical Feature Transfer with Wavelet (HFTW) method ensures robust propagation of core structural information by refining features in the wavelet domain, effectively suppressing noise. Finally, a Selective Feature Integration (SFI) module intelligently merges multi-scale features, combining semantic context with fine-grained detail. Evaluated on a large-scale, domain-specific construction dataset, SAC-Net significantly outperforms state-of-the-art methods, setting a new benchmark in both quantitative metrics and visual quality.