# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 5 Conclusions  
Existing CNN-based super resolution methods have poor high-frequency performance such as outlines and textures because they do not consider high-frequency regions. In addition, existing networks have a greater effect on performance improvement as the number of connections increases, but there is the problem that learning is difficult. To solve this problem, this paper proposed SISR using an attention mechanism that combines channel attention and spatial attention and a feature extraction process with different depths. Convolution was used to extract features from LR images. It is a CSBlock that combines channel attention and spatial attention to emphasize the high-frequency features that have a lot of influence on image quality from the extracted features, and the attention block is composed of 10 CSBlocks. Skip connection was used to effectively solve the gradient vanishing problem. In order to solve the LR insufficient feature problem, different numbers of attention blocks were configured. In order to utilize the extracted features, concatenation was used to connect the features. Afterwards, the feature map was extended using sub-pixel convolution and an HR image was created through residual block and convolution. Through learning using 48 \(\times\) 48 LR image patches and \(L_{1}\) loss, the noise and quality of the super resolution method were improved, and in particular, it showed very good effects on rectilinear patterns, outlines, and object restoration. With the proposed method, high-frequency region features that greatly affect image quality, such as object outlines and textures, were emphasized and various features were extracted. Finally, the proposed method showed about 11% to 26% improvement over the existing bicubic interpolation and about 1 to 2% improvement over VDSR and EDSR. Investigation, D.L., S.Y.C. and S.L. ; Methodology, D.L. ; Software, D.L. and K.J. ; Supervision, K.S. ; Validation, K.J., S.Y.C., S.L. and K.S. ; Writing--original draft, D.L. ; Writing--review and editing, K.J., S.Y.C., S.L. and K.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00922, Development of holographic stereogram printing technology based on multi-view imaging); Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Scale**} & **Bicubic** & **VDSR** & **EDSR** & **Proposed Method** \\  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{4}{*}{Set 5} & \(\times\)2 & 33.66/0.9299 & 37.53/0.9587 & 38.11/**0.9601** & **38.15**/0.9589 \\  & \(\times\)3 & 30.39/0.8682 & 33.66/0.9213 & **34.65**/0.9282 & 34.48/**0.9291** \\  & \(\times\)4 & 28.42/0.8104 & 31.35/0.8838 & 32.46/0.8968 & **32.48**/**0.8988** \\ \hline \multirow{4}{*}{Set 14} & \(\times\)2 & 30.24/0.8688 & 33.03/0.9124 & 33.92/0.9195 & **34.02**/**0.9211** \\  & \(\times\)3 & 27.55/0.7742 & 29.77/0.8314 & **30.52**/**0.8462** & 30.43/0.8431 \\  & \(\times\)4 & 26.00/0.7027 & 28.01/0.7674 & **28.80**/**0.7876** & 28.77/0.7866 \\ \hline \multirow{4}{*}{Urban **100**} & \(\times\)2 & 26.88/0.8403 & 30.76/0.9140 & 32.93/0.9351 & **32.97**/**0.9352** \\  & \(\times\)3 & 24.46/0.7349 & 27.14/0.8279 & **28.80**/**0.8653** & 28.71/0.8640 \\  & \(\times\)4 & 123.14/0.6577 & 25.18/0.7524 & 26.64/0.8033 & **26.68**/**0.8035** \\ \hline \multirow{4}{*}{B 100} & \(\times\)2 & 29.56/0.8431 & 31.90/0.8960 & 35.03/0.9695 & **35.07**/**0.9701** \\  & \(\times\)3 & 27.21/0.7385 & 28.82/0.7976 & **31.26**/**0.9340** & 31.22/0.9337 \\ \cline{1-1}  & \(\times\)4 & 23.96/0.6577 & 27.29/0.7251 & 29.25/0.9017 & **29.27**/**0.9020** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average PSNR/SSIM for scale factor \(\times\)2, \(\times\)3, \(\times\)4.