## 3 Experimental Results
### Visual Quality Evaluation  
In this study, two datasets were tested. One was a small texture dataset, as shown in Figure 6, and the other was BSDS100. Because the proposed method is strong at expressing image structures owing to the use of SARDS, a small texture dataset was prepared to contain various types of image structures, including lines, curves, and regular patterns. The BSDS100 dataset was also tested to verify whether the proposed SALDL could improve the detail representation and dot elimination. Clearly, not all test images were included in the training dataset. Figure 7 shows the experimental results for a small dataset. As shown in the red boxes, the proposed method describes the image structures more accurately. In addition, the overall sharpness of the images was better. In particular, the lines of the pants were restored in more detail and were sharper (as shown in the first row) when using the proposed method. The second row shows more clearly expressed cactus thorns. The third row shows the textures on the palm and the hair accessory in more detail. As shown in the fourth, fifth, sixth, and seventh rows, text including the license plate, rip outline, straw, and [PERSON]'s eyes, respectively, were restored more clearly. Moreover, as shown in the blue box in the fifth row, the proposed method suppressed noisy dots on flat areas, unlike the case involving the conventional DCNN [37] and U-Net [35] methods. The blue box in the last row shows that the proposed method can reproduce smooth skin tones in the face areas, whereas the face areas reconstructed using other methods appeared rougher and noisier. Figure 8 shows other experimental results for the BSDS100 dataset. Similar effects were observed. In other words, in the red box, sharper curves were restored using the proposed method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subnetworks** & **Layers** & **Input Layer** & **Last Convolution Layer** & **Convolution Block (Number)** \\ \hline GCM-based residual subnetwork & \(c=1,\ m=64\) & \(c=64,\ m=1\) & \(c=64,\ m=64(16)\) \\ \hline IRS & \(c=1,\ m=64\) & \(c=64,\ m=1\) & \(c=64,\ m=64(16)\) \\ \hline ISMP including IRS & \(c=1,\ m=64\) & \(c=64,\ m=1\) & \(c=64,\ m=64(22)\) \\ \hline SARDS & \(c=3,\ m=64\) & \(c=64,\ m=1\) & \(c=64,\ m=64(16)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of filters and channels used in convolutional layers. Figure 6: Small texture dataset. Figure 7: Experimental results for a small texture dataset: halffoned images, images reconstructed using DCNN [37], images reconstructed using U-net [35], images reconstructed using DDN [39], images reconstructed using PRL [23; 25], images reconstructed using the proposed SALDL method, and original images (left to right). By comparing the proposed method with the DDN/PRL methods, it was verified that the additional use of the ISMP can improve performance for detailed representation and dot elimination. The DDN directly predicts the continuous-tone images from the base layers, as shown in Equation (11). Because the base layers are predicted, some information may be lost. Hence, the flat areas of the reconstructed images appeared slightly noisy, and their sharpness can be further improved. The PRL method uses input halftone images to increase the amount of information for residual learning, as shown in Equation (3). Therefore, the PRL method can provide results with improved image quality, as compared to the DDN method. However, the PRL method lacks image structure representation. In addition, the existing PRL cannot produce satisfactory results from the same base layers. This reveals that the architecture of the existing PRL is not suitable for low-quality base layers. Hence, the proposed SALDL uses the ISMP to identify Laplacian maps from the base layers to the base layers. Figure 8: Experimental results for BSDS100. input halftoned images. Figure 9 shows the Laplacian maps predicted by the ISMP.