(SARN)spatial-wise attention residual network for image super-resolution

Recent research suggests that attention mechanism is capable of improving performance of deep learning-based single image super-resolution (SISR) methods. In this work, we propose a deep spatial-wise attention residual network (SARN) for SISR. Specifically, we propose a novel spatial attention block (SAB) to rescale pixel-wise features by explicitly modeling interdependencies between pixels on each feature map, encoding where (i.e., attentive spatial pixels in feature map) the visual attention is located. A modified patch-based non-local block can be inserted in SAB to capture long-distance spatial contextual information and relax the local neighborhood constraint. Furthermore, we design a bottleneck spatial attention module to widen the network so that more information is allowed to pass. Meanwhile, we adopt local and global residual connections in SISR to make the network focus on learning valuable high-frequency information. Extensive experiments show the superiority of the proposed SARN over the state-of-art methods on benchmark datasets in both accuracy and visual quality.