Image Super-Resolution Back-Projection Network Based on Holistic Attention
Aiming at the problem of image super-resolution reconstruction,this paper proposes a back-projection network based on the overall attention.The up-and-down iterative projection module and the over-all attention module in the network work together to capture more feature information.Through the mutual conversion of the up and down iterative projection modules,the correlation between different resolution fea-tures is obtained,which helps to learn high-frequency texture details.In this paper,the layer attention module is used to weight the attention between the layers of the feature layers output by the upper and lower projec-tions to find the interdependent relationship between different layers,channels and positions.In addition,the spatial channel attention module is used for the final up-projection module,which can guide the network to learn relevant information within and between channels.Ablation experiments show that the enhancement of feature information by the two attention modules helps to improve the reconstruction effect.Comparative ex-periments show that the back-projection network model based on holistic attention proposed in this paper has a good reconstruction effect.
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