# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 4 Experimental Results  
For the network learning proposed in this paper, it was trained using the DIV2K dataset consisting of 800 HR images and the \(L_{1}\) loss. Figure 12: Upsampling. \[L_{1}\big{(}x,\ f\big{(}x^{\prime}\big{)}\big{)}=\|x-f\big{(}x^{\prime}\big{)}\|, \tag{17}\]  
Equation (17) is the \(L_{1}\) loss, where \(x\) is the original image, \(x^{\prime}\) is the bicubic reduced image, and \(f(\cdot)\) is the network of the proposed method. The loss function is used to measure the difference between the ground truth and the output of the network. If the value of the loss function decreases, the prediction is accurate. Adam optimizer [26] was used for learning, and the parameters used for learning consisted of learning rate, \(\beta_{1}\), and \(\beta_{2}\), respectively. \(\beta_{1}\) is the exponential decay rate for the first-order moment estimate, and \(\beta_{2}\) is the exponential decay rate for the second-order moment estimate. The default value is \(10e^{-4}\) for learning rate, 0.9 for \(\beta_{1}\), and 0.999 for \(\beta_{2}\). The input image was composed of 48 \(\times\) 48 patches of LR image size. In this paper, bicubic interpolation, VDSR, and EDSR were used based on the Set5, Set14, B100, and Urban100 datasets to compare the proposed method. Figure 13 shows an image of the DIV2K Validation PSNR results trained 50 epochs by the number of attention blocks in Show Feature Extraction and Deep Feature Extraction. As the number of attention blocks increased, the performance increased, and when the number of attention blocks exceeded a certain number of attention blocks, the PSNR decreased. Through the experiment, the number of attention blocks in the Show Feature Extraction with the highest PSNR was three and the number of attention blocks in the Deep Feature Extraction was 10 applied. Figure 14 is a DIV 2K Validation result graph according to the number of CSBlocks constituting the attention block. As the number of CSBlocks increases, the PSNR value increases. However, when the number of CSBlocks exceeds 10, the PSNR value decreases. When a certain level of depth is exceeded, learning difficulties occur, and performance deteriorates even though many parameters are used. For the experiment, 10 CSBlocks with the highest PSNR were applied. Figure 15 is the four-times result image of img052 of the Urban100 dataset. Peak signal-to-noise ratio (PSNR) is the ratio of noise to the maximum value of the image and is used to measure the image quality loss. The higher the PSNR value, the lower the loss compared to the original video. The structural similarity index measure (SSIM) is a metric developed for the purpose of evaluating human perceptual ability [27]. Human vision is specialized in deriving structural information of images, so structural information has the greatest impact on image quality. Luminance, contrast, and structure are used to measure this, and the larger the value of SSIM, the smaller the loss. Unlike other deep learning algorithms, bicubic interpolation restores the values of 16 neighboring pixels as a two-dimensional formula, so the image is natural, but the result is blurry due to lack of high-frequency region processing. VDSR performed better on building contours than bicubic interpolation using 20 convolutions and residual learning. EDSR shows clear results by using 69 convolutions that are deeper than existing VDSR. The results obtained through proposed CSBlock and attention block methods show improved results in the high-frequency region compared to  
Figure 13: Results the number of Attention Blocks. existing networks. However, numerically, it shows a value similar to EDSR. However, in the case of EDSR, there is a lack of emphasis on specific high-frequency features because it is a structure that only builds convolutions. However, the method we propose optimizes the network and has the advantage of enabling high-speed learning as well as specific high-frequency regions. Figure 16 is the result of magnifying PPT3 of Set14 by four times.