  This paper proposes Deep Bi-Dense Networks (DBDN) for single image
super-resolution. Our approach extends previous intra-block dense connection
approaches by including novel inter-block dense connections. In this way,
feature information propagates from a single dense block to all subsequent
blocks, instead of to a single successor. To build a DBDN, we firstly construct
intra-dense blocks, which extract and compress abundant local features via
densely connected convolutional layers and compression layers for further
feature learning. Then, we use an inter-block dense net to connect intra-dense
blocks, which allow each intra-dense block propagates its own local features to
all successors. Additionally, our bi-dense construction connects each block to
the output, alleviating the vanishing gradient problems in training. The
evaluation of our proposed method on five benchmark datasets shows that our
DBDN outperforms the state of the art in SISR with a moderate number of network
parameters.
