# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 4 Experimental Results
In bicubic interpolation, the boundary line of the curved part of the letter was restored blurry. VDSR has clearly restored boundary lines, but shows distorted results in some curves. The EDSR method  
Figure 16: Visual evaluation for a scale factor of 4\(\times\) on the image “8023” from B100: (**a**) Ground Truth (PSNR, SSIM); (**b**) Bicubic (30.70, 0.7447); (**c**) VDSR (34.28, 0.8699); (**d**) EDSR (35.85, 0.8832); (**e**) Proposed (**35.91, 0.8851**). Figure 14: Results of the number of CSBlocks. Figure 15: Visual evaluation for a scale factor of 4\(\times\) on the image “img052” from Urban100: (**a**) Ground Truth (PSNR, SSIM); (**b**) Bicubic (28.24, 0.7311); (**c**) VDSR (32.57, 0.8875); (**d**) EDSR (35.11, 0.9319); (**e**) Proposed (**35.13, 0.9322**). shows a better restoration result from distortion than the existing VDSR. The proposed method also shows improved results on the contour. Figure 18 is the result of magnifying the COMIC of Set14 by four times. Bicubic interpolation has blurred outlines due to complex and small patterns, and the small patterns have disappeared. VDSR is improved over bicubic interpolation in the small missing patterns of objects, but the outlines are blurry. EDSR contours have been improved and object behavior has been restored. The proposed method has improved contour lines and shows good numerical results. Table 1 is a table comparing PNSR and SSIM for each dataset. The first Set5 is a dataset consisting of five images. The image of Set5 is a dataset with a simple pattern and shows higher results in PSNR and SSIM than other datasets in most algorithms. The results obtained through the proposed method show a difference of more than 4%. The second Set14 is a dataset consisting of 14 images of various environments. It is an image dataset similar to Set5 and has lower PNSR and SSIM values than Set5 dataset due to various environments and a more complex dataset than Set5, and showed similar results to Set5. The third Urban100 is a dataset consisting of 100 architectural images. Due to the nature of building images, there are small patterns such as small windows and bricks, and it is important to use high frequencies and estimate loss areas compared to other datasets. The proposed method shows higher performance than the existing EDSR and VDSR methods by utilizing high-frequency components and various features. Finally, B100 is a dataset of 100 objects composed of various shapes such as plants, people, and food in natural  
Figure 17: Visual evaluation for a scale factor of 4\(\times\) on the image “PPT3” from Set14: (**a**) Ground Truth (PSNR, SSIM); (**b**) Bicubic (24.31, 0.7889); (**c**) VDSR (29.93, 0.9344); (**d**) EDSR (31.51, 0.9594); (**e**) Proposed (**31.53, 0.9593**). Figure 18: Visual evaluation for a scale factor of 4\(\times\) on the image “COMIC” from Set14: (**a**) Ground Truth (PSNR, SSIM); (**b**) Bicubic (24.05, 0.4994); (**c**) VDSR (27.26, 0.6988); (**d**) EDSR (28.42, 0.7606); (**e**) Proposed (**28.55, 0.7613**). images. Various objects have a lot of irregular shapes rather than regular shapes like the Urban100 dataset, so learning of various patterns has a lot of influence. When using the proposed method, it shows higher performance than the existing network. As a result, our proposed method shows better performance than existing algorithms when there are many high-frequency regions and various patterns exist.