Deep Back-Projection Networks For Super-Resolution

Understand

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 down-sampling layers, providing an error feedback mechanism for projection errors at each stage.
• We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components.