Abstract:  Hierarchical features extracted from convolutional neural networks contain affluent semantic information and they are crucial for image reconstruction. However， some existing image super-resolution reconstruction methods are incapable of excavating detailed enough hierarchical features in convolutional network. Therefore， we propose a model termed spatial attention residual network （SARN） to relieve this issue. Specifically， we design a spatial attention residual block （SARB）， the enhanced spatial attention （ESA） is embedded into SARB to obtain more effective high-frequency information. Secondly， feature fusion mechanism is introduced to fuse the features derived from each layer. Thereby， the network can extract more detailed hierarchical features. Finally， these fused features are fed into the reconstruction network to produce the final reconstruction image. Experimental results demonstrate that our proposed model outperforms the other algorithms in terms of quantitative evaluation and visual comparisons. That indicates our model can effectively utilize the hierarchical features contained in the image， thus achieve a better super-resolution reconstruction performance.

Key words: Key words: super-resolution reconstruction, spatial attention, residual network, feature fusion mechanism, hierarchical features