TY  - JOUR
AU  - Haider, Asad Ullah
AU  - Gazis, Alexandros
AU  - Zahoor, Faryal
PY  - 2026
DA  - 2026/02/19
TI  - SemanticBlur: Semantic-Aware Attention Network with Multi-Scale Feature Refinement for Defocus Blur Detection
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 3
IS  - 1
SP  - 21
EP  - 31
DO  - 10.62762/TIS.2025.879161
UR  - https://www.icck.org/article/abs/TIS.2025.879161
KW  - blur detection
KW  - semantic segmentation
KW  - attention mechanisms
KW  - deep neural networks
KW  - image analysis
KW  - multi-scale processing
AB  - 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.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Haider2026SemanticBl,
  author = {Asad Ullah Haider and Alexandros Gazis and Faryal Zahoor},
  title = {SemanticBlur: Semantic-Aware Attention Network with Multi-Scale Feature Refinement for Defocus Blur Detection},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {21-31},
  doi = {10.62762/TIS.2025.879161},
  url = {https://www.icck.org/article/abs/TIS.2025.879161},
  abstract = {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.},
  keywords = {blur detection, semantic segmentation, attention mechanisms, deep neural networks, image analysis, multi-scale processing},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}