Rain-Resilient Image Restoration for Reliable and Sustainable Visual Monitoring in Industrial Inspection and Quality Control
Miao Zhang, Shanqin Wang, Shiqun Yin · Processes · 2025
Reliable visual monitoring is essential for industrial quality control systems under adverse weather conditions. Rain-induced degradation, such as occlusions, texture blurring, and depth distortions, can significantly hinder image clarity and compromise precision in surface defect detection. To address this, we propose a novel image deraining framework, the Degradation-Background Perception Network (DBPNet). DBPNet features a hierarchical encoder–decoder structure and incorporates two core modules: the Frequency Degradation Perception Module (FDPM) and the Depth Background Perception Module (DBPM). FDPM focuses on frequency decomposition to remove high-frequency rain streaks while retaining critical image features using cross-attention mechanisms. DBPM is proposed to integrate robust depth maps, which remain unaffected by rain degradation, as explicit constraints to guide the model in reconstructing clean scenes. Furthermore, we propose the Selective Focus Attention (SFA) module, which enhances interactions between frequency-domain features and background priors, ensuring accurate reconstruction and effective rain removal. Experimental results on five synthetic and real-world benchmark datasets demonstrate that our method outperforms state-of-the-art CNN and transformer-based approaches. This framework contributes to more robust visual input for process control, enabling better fault detection, predictive maintenance, and sustainable system operation.