  The deep convolutional neural networks (CNNs) using attention mechanism have
achieved great success for dynamic scene deblurring. In most of these networks,
only the features refined by the attention maps can be passed to the next layer
and the attention maps of different layers are separated from each other, which
does not make full use of the attention information from different layers in
the CNN. To address this problem, we introduce a new continuous cross-layer
attention transmission (CCLAT) mechanism that can exploit hierarchical
attention information from all the convolutional layers. Based on the CCLAT
mechanism, we use a very simple attention module to construct a novel residual
dense attention fusion block (RDAFB). In RDAFB, the attention maps inferred
from the outputs of the preceding RDAFB and each layer are directly connected
to the subsequent ones, leading to a CCLAT mechanism. Taking RDAFB as the
building block, we design an effective architecture for dynamic scene
deblurring named RDAFNet. The experiments on benchmark datasets show that the
proposed model outperforms the state-of-the-art deblurring approaches, and
demonstrate the effectiveness of CCLAT mechanism.
