## 6 Discussion  
In this paper, we proposed a method to improve the infrared image colorization problem by using a CGAN network that integrates multi-scale features into the generator and adds an attention mechanism to the discriminator. Prior literature has also attempted to improve networks using multi-scale feature modules and attention mechanisms [22; 23; 31]. For example, [PERSON]. [31] proposed a multi-scale residual attention network (MsRAN), while [PERSON]. [22] integrated multi-scale attention mechanisms into the generator and discriminator of a GAN to fuse infrared and visible light images (AttentionFGAN) and [23] proposed a deep network that concatenates feature learning modules and fusion learning mechanisms for infrared and visible light image fusion.  
The main improvement direction for infrared image colorization is to focus the network's attention on the most important areas of the image and retain more texture infor  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PSNR (dB)** & **SSIM** \\ \hline Pix2 pixCGAN & 15.6913 & 0.5633 \\ Add attention mechanism & 15.9534 & 0.6111 \\ Multi-scale feature fusion & 15.9724 & 0.6161 \\ Add attention and multi-scale feature fusion & 16.5342 & 0.6385 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of evaluation indexes for colorization of NIR images.  
Figure 12: Experimental results of NIR image colorization based on attention mechanism and multi-scale features. (**a**) Real image; (**b**) NIR image; (**c**) pix2 pixCGAN; (**d**) attention mechanism; and (**e**) attention + multi-scale feature fusion.  
mation. We also used multi-scale feature modules and attention mechanism modules but, unlike previous studies, we chose to improve the CGAN network. The CGAN network is more suitable for tasks that require generating images with specified conditional information than the GAN network. Infrared image colorization tasks require generating color images, and images generated solely by the GAN generator may have issues such as unnatural colors, blurriness, and distortion. The CGAN generator can generate corresponding color images based on the input infrared images, and the input infrared images as conditional information can help the generator produce more accurate corresponding color images, thus solving the above issues. We then added the multi-scale feature module and attention mechanism to the generator and discriminator separately, rather than adding both modules to the generator or discriminator simultaneously. Our goal was to use the game theory of GAN networks, allowing the generator and discriminator networks to compete and promote each other using different methods.  
We selected a dataset that included many images with texture details, such as buildings with tightly arranged windows and dense trees. However, due to our focus on making the edges of the subject object smaller and clearer to solve color leakage and edge blur problems, there may be some deviation in the background color of the sky in some images. In future work, we will consider pre-processing the images before inputting them into the CGAN network to enhance the image quality and color restoration ability. After generating color images, post-processing can also be applied to the output images, such as denoising, smoothing, and enhancing contrast, to improve the quality and realism of the output images. The quality and quantity of the dataset are also crucial for the effectiveness of infrared image colorization. Therefore, future research can try to collect more high-quality infrared image datasets and conduct more in-depth studies based on the dataset.