# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
## 4 Simulation Results  
To evaluate the performance of the proposed algorithm, the synthesis results using various synthesis maps were obtained and compared. In addition, the results obtained using the multilayer resolution synthesis method, which is a representative conventional visible-light near-infrared synthesis method, and a deep-learning-based synthesis method were compared. The daytime images used in the experiment were taken by alternately applying an IR cut filter and a VIS cut filter, and the dataset published by EPFL was used [14]. Therefore, the input images were acquired as visible-light and infrared images. For the night image, an image captured by switching the IR auxiliary light with the IR cut filter removed using a broadband camera module with the image sensor light receiving band ranging from visible to near-infrared (approximately 1000 nm) was used. In this paper, a multiplex imaging system was fabricated to capture IR and visible-light images at night. The proposed system captures two types of IR images, high-intensity IR and low-intensity IR by alternating the intensity of the IR lamp. The IR lamps are synchronized with the capturing period. In addition to the IR LED array of CCTV at night, four 5W-1000 nm IR LEDs are used for IR flash, and are turned on only when capturing high-intensity images. When the IR flash is on, the luminance channel image is taken, and when the IR flash is off, the RGB 3 channel image is taken. Figure 11 shows the sequence of NIR and visible image switching capturing at night. The input image was acquired as a visible-light image and a broadband image. A broadband image refers to an image in which visible-light and near-infrared images are combined, and because the color channel of the broadband image is not used in this study, it is used synonymously with the near-infrared image. Figure 12 shows a comparison of the performance of the synthesis results according to the synthesis maps of the various base layers. It shows the resulting image using the bilateral filter map, joint bilateral filter map, and proposed synthesis map. As can be seen from the figure, the resulting image using the proposed base layer synthesis map contains detailed information inside the cup and box. Figures 13-15 show the synthesis results for the daytime images. Compared with the Densfuse synthesis method, it is confirmed that the proposed method has excellent visibility of detailed information in the area because the luminance range of the base layer is well-compressed in the shaded area of the visible-light image. In addition, in the Vanmali and PCA synthesis method, colors are shown differently than in the original image, such as a strong red color, over-saturation symptoms, and mismatched white balance throughout the image, including buildings and roofs. In contrast, in the proposed method, the original color of the visible-light image is naturally expressed without oversaturation. In addition, it can be confirmed that the proposed method expresses detailed information better than the two existing synthesis methods in the details of the mountain in Figure 13, the boundary information of the leaves in Figure 14, and the details of the wave in Figure 15.  
Figure 12: Comparison of various synthesis results: (**a**) result with a bilateral filter map, (**b**) result with an associative bilateral filter map, (**c**) result with a proposed synthesis map. Figure 13: Cont. Figure 14: Daytime input and result images (2): (a) visible-light image and near-infrared image, (b) Densefuse method result, (c) Vanmali method result, (d) PCA method result, (e) proposed method result. Figure 13: Daytime input and result images (1): (a) visible-light image and near-infrared image, (b) Densefuse method result, (c) Vanmali method result, (d) PCA method result, (e) proposed method result. Figures 16-18 show a performance comparison for the night images.