SemanticBlur: Semantic-Aware Attention Network with Multi-Scale Feature Refinement for Defocus Blur Detection

Defocus blur detection is essential for computational photography applications, but existing methods struggle with accurate blur localization and boundary preservation. We propose SemanticBlur, a deep learning framework which integrates semantic understanding with attention mechanisms for robust defocus blur detection. Our semantic-aware attention module combines channel attention, spatial attention, and semantic enhancement to leverage high-level features for low-level feature refinement. The architecture employs a modified ResNet-50 backbone with dilated convolutions that preserves spatial resolution while expanding receptive fields, coupled with a feature pyramid decoder using learnable fusion weights for adaptive multi-scale integration. A combined loss function balancing binary cross-entropy and structural similarity achieves both pixel-wise accuracy and structural coherence. Extensive experiments on four benchmark datasets (CUHK, DUT, CTCUG, EBD) demonstrate state-of-the-art (SOTA) performance, with ablation studies confirming that semantic enhancement provides the most significant gains while maintaining computational efficiency. SemanticBlur generates visually coherent detection maps with sharp boundaries, validating its practical applicability for real-world deployment.