Abstract
The feed-forward architectures of recently proposed
deep super-resolution networks learn representations of
low-resolution inputs, and the non-linear mapping from
those to high-resolution output. However, this approach
does not fully address the mutual dependencies of low- and
high-resolution images. We propose Deep Back-Projection
Networks (DBPN), that exploit iterative up- and downsampling layers, providing an error feedback mechanism
for projection errors at each stage. We construct mutuallyconnected up- and down-sampling stages each of which
represents different types of image degradation and highresolution components. We show that extending this idea
to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct
further improve super-resolution, yielding superior results
and in particular establishing new state of the art results
for large scaling factors such as 8× across multiple data
sets