## 5 Experimental Results and Evaluation
### Experiment Results
#### 5.3.1 Experimental Results of NIR Image Colorization Based on Multi-Scale Features
After improving both the generator and the discriminator, (e) has better results than before, the distinction between the building and the background is more obvious, the building color is closer to the real color, the overall color is more consistent than the first two methods of coloring, which effectively improves the blurred parts of the image, and the overall style is not coordinated. It can be seen from the second group of images in figure (c) that, color leakage in the background of the red box section caused color distortion in the tower after colorization, so that the top of the tower has some blue. And due to the influence of the semantic being unclear when coloring, the tower and the sky border is relatively blurred. The difference between the object and the background in (d) and (e) is more obvious and the contrast is a bit greater. The objects in the figure have more detailed information and more obvious features. The infrared image is able to show the objects that the visible image cannot present due to shadow exposure and other reasons, and the object outline is clearer after colorization of the NIR image by the method in this paper, as shown in the red box in (e), and it gives the color that is consistent with the real image. After the experiments, it was proved that the improved network in this paper has improved both color leakage and semantic ambiguity, and the colorized images have good results visible to the naked eye. (2) Quantitative analysis  
The experimental results are evaluated using the two previously mentioned metrics of peak signal-to-noise ratio and structural similarity. The PSNR focuses on the distortion level of the color image obtained after using network coloring, and its value is negatively correlated with the distortion level, with larger values indicating that the distortion level is lower and the image obtained is more closely matched to the real image. SSIM measuresthe similarity between the generated image and the real image from three aspects: brightness, contrast, and structure, which is calculated and evaluated based on the structural information of the image and is more in line with the subjective human visual perception. Accordingly, the closer the value of SSIM is to 1, the more consistent the two image structures are, and the more similar the image after network coloring is to the real one, which proves that the network is more effective. From Table 4, it can be observed that both the generator-improved network and the discriminator-improved network show some improvement in the above-mentioned two metrics after colorizing NIR images. This suggests that the corresponding issues have been addressed to some extent by the improved networks. The network that adds both attention and multi-scale feature fusion works best, and both metrics have higher values. The experiments show that, both in terms of subjective human perception and objective evaluation metrics, the method used significantly improves the color leakage and semantic uncertainty of the original network for infrared coloring.