# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
## 6 Simulations  
The proposed resulting images were evaluated against images obtained using conventional methods. As conventional methods, we considered the Laplacian fusion algorithm [1] and the low-rank fusion algorithm [5]. We used input images (i.e., reference images of the conventional method [1]). Both VIS and NIR input images were captured in the daytime. The conventional method (i.e., the low-rank fusion algorithm [5]) was adapted to the proposed color compensation algorithm to compare the Laplacian fusion algorithm [1] with the proposed method for color images. The resulting images of the low-rank conventional method (d) contained noise, as shown in Figures 6-12. Furthermore, we found that compared to the aforementioned conventional methods, the proposed method better represents the edges of objects such as clouds, trees, leaves, and mountains. If an object is clear in the input VIS image, the output images of the proposed method will preserve the hue of the color information represented in the input VIS image. Figure 6: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. Figure 8: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. Figure 7: Input image pair and resulting images: (**a**) input VIS and near-infrared (NIR) images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. Figure 10: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. Figure 9: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. The resulting values were calculated from Table 1 to evaluate the image quality and image sharpness for comparing the results obtained using the conventional and proposed methods. Feature mutual information (FMI) [15] is a method of calculating image-fusion quality based on the calculation of  
Figure 11: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. Figure 12: Input image pair and resulting images: (**a**) input VIS and NIR images, (**b**) Laplacian pyramid fusion [1], (**c**) low-rank fusion [5], and (**d**) the proposed method. information contained in the two input images. S3 [16] is the measurement criteria for image clarity, and it is measured according to the locally recognized sharpness of the image. In Table 1 and Figure 13, a high two-metric score means that the image is of good quality. The average score of the image evaluation indicator for the proposed method is higher than that of the conventional method. The FMI method of evaluating fusion quality has higher scores than conventional methods. The S3 method of fusion quality evaluation, i.e., sharpness evaluation, has higher scores than the conventional methods. Thus, compared with the results of the conventional methods, the results of the proposed method were evaluated to be much better in terms of picture quality