Deep Back-Projection Networks for Super-Resolution

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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
more » ... nected 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.
doi:10.1109/cvpr.2018.00179 dblp:conf/cvpr/HarisSU18 fatcat:bo24jbi6sjanzmqn6qtstctt4y