Hierarchical edge-aware network for defocus blur detection

Defocus blur detection (DBD) aims to separate blurred and unblurred regions for a given image. Due to its potential and practical applications, this task has attracted much attention. Most of the existing DBD models have achieved competitive performance by aggregating multi-level features extracted from fully convolutional networks. However, they also suffer from several challenges, such as coarse object boundaries of the defocus blur regions, background clutter, and the detection of low contrast focal regions. In this paper, we develop a hierarchical edge-aware network to solve the above problems, to the best of our knowledge, it is the first trial to develop an end-to-end network with edge awareness for DBD. We design an edge feature extraction network to capture boundary information, a hierarchical interior perception network is used to generate local and global context information, which is helpful to detect the low contrast focal regions. Moreover, a hierarchical edge-aware fusion network is proposed to hierarchically fuse edge information and semantic features. Benefiting from the rich edge information, the fused features can generate more accurate boundaries. Finally, we propose a progressive feature refinement network to refine the output features. Experimental results on two widely used DBD datasets demonstrate that the proposed model outperforms the state-of-the-art approaches.