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.

翻译：散焦模糊检测（DBD）旨在分离图像中的对焦区域与失焦区域。现有方法常将对焦的均匀区域误判为散焦模糊区域，这很可能源于未考虑导致散焦模糊的内在因素。受深度、景深（DOF）及散焦原理的启发，我们提出一种名为D-DFFNet的方法，该方法以隐式方式融合深度与景深线索，使模型能以更自然的方式理解散焦现象。我们提出深度特征蒸馏策略，从预训练的单目深度估计模型中获取深度知识，并采用景深边缘损失函数来理解景深与深度之间的关系。在公开基准数据集及新构建的大规模基准数据集EBD上，本方法均优于现有最优技术。源代码及EBD数据集可通过以下链接获取：https:github.com/yuxinjin-whu/D-DFFNet。