  Deep learning based image Super-Resolution (SR) has shown rapid development
due to its ability of big data digestion. Generally, deeper and wider networks
can extract richer feature maps and generate SR images with remarkable quality.
However, the more complex network we have, the more time consumption is
required for practical applications. It is important to have a simplified
network for efficient image SR. In this paper, we propose an Attention based
Back Projection Network (ABPN) for image super-resolution. Similar to some
recent works, we believe that the back projection mechanism can be further
developed for SR. Enhanced back projection blocks are suggested to iteratively
update low- and high-resolution feature residues. Inspired by recent studies on
attention models, we propose a Spatial Attention Block (SAB) to learn the
cross-correlation across features at different layers. Based on the assumption
that a good SR image should be close to the original LR image after
down-sampling. We propose a Refined Back Projection Block (RBPB) for final
reconstruction. Extensive experiments on some public and AIM2019 Image
Super-Resolution Challenge datasets show that the proposed ABPN can provide
state-of-the-art or even better performance in both quantitative and
qualitative measurements.
