  Defocus blur detection (DBD) separates in-focus and out-of-focus regions in
an image. Previous approaches mistakenly mistook homogeneous areas in focus for
defocus blur regions, likely due to not considering the internal factors that
cause defocus blur. Inspired by the law of depth, depth of field (DOF), and
defocus, we propose an approach called D-DFFNet, which incorporates depth and
DOF cues in an implicit manner. This allows the model to understand the defocus
phenomenon in a more natural way. Our method proposes a depth feature
distillation strategy to obtain depth knowledge from a pre-trained monocular
depth estimation model and uses a DOF-edge loss to understand the relationship
between DOF and depth. Our approach outperforms state-of-the-art methods on
public benchmarks and a newly collected large benchmark dataset, EBD. Source
codes and EBD dataset are available at: https:github.com/yuxinjin-whu/D-DFFNet.
