  Convolutional Neural Network (CNN) is intensively implemented to solve super
resolution (SR) tasks because of its superior performance. However, the problem
of super resolution is still challenging due to the lack of prior knowledge and
small receptive field of CNN. We propose the Segmentation-Piror Self-Attention
Generative Adversarial Network (SPSAGAN) to combine segmentation-priors and
feature attentions into a unified framework. This combination is led by a
carefully designed weighted addition to balance the influence of feature and
segmentation attentions, so that the network can emphasize textures in the same
segmentation category and meanwhile focus on the long-distance feature
relationship. We also propose a lightweight skip connection architecture called
Residual-in-Residual Sparse Block (RRSB) to further improve the
super-resolution performance and save computation. Extensive experiments show
that SPSAGAN can generate more realistic and visually pleasing textures
compared to state-of-the-art SFTGAN and ESRGAN on many SR datasets.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing