  Defocus Blur Detection(DBD) aims to separate in-focus and out-of-focus
regions from a single image pixel-wisely. This task has been paid much
attention since bokeh effects are widely used in digital cameras and smartphone
photography. However, identifying obscure homogeneous regions and borderline
transitions in partially defocus images is still challenging. To solve these
problems, we introduce depth information into DBD for the first time. When the
camera parameters are fixed, we argue that the accuracy of DBD is highly
related to scene depth. Hence, we consider the depth information as the
approximate soft label of DBD and propose a joint learning framework inspired
by knowledge distillation. In detail, we learn the defocus blur from ground
truth and the depth distilled from a well-trained depth estimation network at
the same time. Thus, the sharp region will provide a strong prior for depth
estimation while the blur detection also gains benefits from the distilled
depth. Besides, we propose a novel decoder in the fully convolutional
network(FCN) as our network structure. In each level of the decoder, we design
the Selective Reception Field Block(SRFB) for merging multi-scale features
efficiently and reuse the side outputs as Supervision-guided Attention
Block(SAB). Unlike previous methods, the proposed decoder builds reception
field pyramids and emphasizes salient regions simply and efficiently.
Experiments show that our approach outperforms 11 other state-of-the-art
methods on two popular datasets. Our method also runs at over 30 fps on a
single GPU, which is 2x faster than previous works. The code is available at:
https://github.com/vinthony/depth-distillation
