# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 4 Experimental Results  
As shown in Figure 4, we used the DIVerse 2K (DIV2K) dataset [24] whose total number is 800 images to train the proposed methods. In order to design SR-LNN capable of up-sampling input images four times, all training images with RGB components are converted into YUV components and extracted only Y component with the size of \(100\times 100\) patch without overlap. In order to generate interpolated input images, the patches are down-sampled and then up-sampled it again by bi-cubic interpolation. Finally, we obtained three training datasets from DIV2K where the total number of each training dataset is 210,048 images for original images, low-resolution images, and interpolated low-resolution images, respectively. For testing our SR-LNN models, we used Set5, Set14, Berkeley Segmentation Dataset 100 (BSD100), and Urban100 as depicted in Figure 5, which are representatively used as testing datasets in most SR studies. For reference, Set5 was also used as a validation dataset. All experiments were run on an Intel Xeon Skylake (eight cores @ 2.59 GHz) having 128 GB RAM and two NVIDIA Tesla V100 GPUs under the experimental environment described in Table 5. After setting a bicubic interpolation method as an anchor for performance comparison, we compared the proposed two SR-LNN models with SR-CNN [9], AR-CNN [14], and SR-DenseNet [18] in terms of image quality enhancement and network complexity. Figure 4: Training dataset. (DIV2K). Figure 5: Test datasets. (Set5, Set14, BSD100, and Urban100). In order to evaluate the accuracy of SR, we used PSNR and the structural similarity index measure (SSIM) [25; 26] on the Y component as shown in Tables 6 and 7, respectively. In general, PSNR has been commonly used as a fidelity measurement and it is the ratio between the maximum possible power of an original signal and the power of corrupting noise that affects the fidelity of its representation. In addition, SSIM is a measurement that calculates a score using structural information of images and is evaluated as similar to human perceptual scores. Compared with the anchor, the proposed SR-ILLNN and SR-SLNN enhance PSNR by as many as 1.81 decibel (dB) and 1.71 dB, respectively. Similarly, the proposed SR-LNNs show significant PSNR enhancement, compared to SR-CNN and AR-CNN. In contrast to the results of the anchor, the proposed SR-ILLNN has similar PSNR performance on most test datasets, compared with SR-DenseNet. In addition, we conducted an experiment to verify the effectiveness of skip connections and dense connections. In particular, the more dense connections are deployed in the between convolution layers, the more network parameters are required in the process of convolution operations. Table 8 shows the results of tool-off tests on the proposed methods. As both skip connections and dense connections contribute to improve PSNR in the test datasets, the proposed methods are deployed these schemes. Figure 6 shows MSE as well as PSNR corresponding to the number of epochs and these experiments were evaluated from all comparison methods (SR-CNN, AR-CNN, and SR-DenseNet), including the proposed methods. It is confirmed that although SR-DenseNet has the highest reduction-rate in terms of MSE, the proposed methods have an almost similar increase rate in terms of PSNR. Figure 7 shows the comparisons of subjective quality between the proposed methods and previous methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Bicubic** & **SR-CNN [9]** & **AR-CNN [14]** & **SR-DenseNet [18]** & **SR-ILLNN** & **SR-SLNN** \\ \hline Set5 & 28.44 & 30.30 & 30.35 & 31.43 & 31.41 & 31.29 \\ Set14 & 25.80 & 27.09 & 27.10 & 27.84 & 27.83 & 27.73 \\ BSD100 & 25.99 & 26.86 & 26.86 & 27.34 & 27.33 & 27.28 \\ Urban100 & 23.14 & 24.33 & 24.34 & 25.30 & 25.32 & 25.18 \\ Average & 24.73 & 25.80 & 25.81 & 26.53 & 26.54 & 26.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average results of PSNR (dB) on the test dataset.