## 5 Experimental Results and Evaluation
### Experiment Results  
#### 5.3.1 Experimental Results of NIR Image Colorization Based on Multi-Scale Features  
The comparison experiments are first conducted on the generative network U-Net after adding the improved multi-scale feature fusion module. Previously, we added the multi-scale feature fusion module to the generator composed of U-Net, and this improvement enhanced the network's understanding of semantic features and effectively improved the color leakage. As the experimental results in Figure 11 show, the images generated by the multi-scale feature-based method do not have the problem of color leakage, and the colors of the images look natural. The comparison images in this experiment are colored by the CGAN network pix2 pix. The image obtained from the base network in the first group, the sky color leaks during the coloring process, resulting in a blue window color in the red frame; adding  
Figure 11: Experimental results of NIR image colorization based on multi-scale features. (**a**) Real image; (**b**) NIR image; (**c**) base; and (**d**) multi-scale feature fusion. multi-scale feature fusion network on the image coloring looks very natural, and there is no color spillover situation. In addition, compared with the coloring of the baseline network, the outline of the vehicle in front of the house is clearer and the color contrast has been improved very well. The second group of images colored by the baseline network shows that the house in the red box is very blurred due to the shadow, and it is not even possible to distinguish the object as a house. The outline of the house after being colored with the improved network of multi-scale feature fusion is very clear, and the shadow color is also clearly distinguished from the near part, which shows that the improved network is better than the previous network for semantic extraction. The third group of images shows that the images colored with multi-scale feature fusion are clearer, and the boundaries of trees and fine branches are clearly represented. However, the images colored with baseline have blurred contours, white color, and blue leakage. From the above comparison, although the network with the multi-scale feature fusion module still cannot fully restore the color of the real image, it obviously improves the problem of color leakage that exists in other methods and makes the contour boundary of the object clearer. 3.2 Experimental Results of NIR Image Colorization Based on Attention Mechanism and Multi-Scale Features  
(1) Qualitative analysis  
The experimental objects are the following sets of images, where (a) are real images, (b) are NIR images, (c) are images colored with the Pix2 PixCGAN network, (d) are the coloring effects of adding channel attention and spatial attention modules to the discriminator, and (e) are the coloring effects of the network after adding multi-scale feature fusion to the generator and improving the discriminator with the attention mechanism at the same time. As can be seen from the Figure 12, the image (c) in the first group has a lot of noise in the red box part, and the contour and edge information of the image is not clear enough, in addition to the obvious color leakage at the edges of the columns. However, in the images (d) and (e) obtained by the improved network, the image clarity is improved and the edges are more obvious. After adding the attention mechanism, (d) has a deeper outline than (c), and the situation where the tip of the tower is not well distinguished from the background has been improved, and the overall color of the building theme is more harmonious, without the problem of the bluish color caused by color leakage.