  Deep image completion usually fails to harmonically blend the restored image
into existing content, especially in the boundary area. This paper handles with
this problem from a new perspective of creating a smooth transition and
proposes a concise Deep Fusion Network (DFNet). Firstly, a fusion block is
introduced to generate a flexible alpha composition map for combining known and
unknown regions. The fusion block not only provides a smooth fusion between
restored and existing content, but also provides an attention map to make
network focus more on the unknown pixels. In this way, it builds a bridge for
structural and texture information, so that information can be naturally
propagated from known region into completion. Furthermore, fusion blocks are
embedded into several decoder layers of the network. Accompanied by the
adjustable loss constraints on each layer, more accurate structure information
are achieved. We qualitatively and quantitatively compare our method with other
state-of-the-art methods on Places2 and CelebA datasets. The results show the
superior performance of DFNet, especially in the aspects of harmonious texture
transition, texture detail and semantic structural consistency. Our source code
will be avaiable at: \url{https://github.com/hughplay/DFNet}
