## 1 Introduction
[18] tackled the challenge of modal differences between infrared and visible images by generating grayscale images as auxiliary information and using point-by-point transformation for single-channel infrared images. Traditional grayscale image coloring methods have a complicated workflow that cannot be applied to colorizing NIR images. The GAN network, with its unique network structure and training mechanism, has been widely applied by researchers in the field of infrared image coloring [19; 20; 21; 22]. [PERSON] et al. [19] proposed an improved Dual GAN architecture that uses two deep learning networks to establish the translation relationship between NIR and RGB images without the need for prior image pairing and labeling. [PERSON] et al. [20] proposed a DenseUnet generative adversarial network for colorizing near-infrared (facial) images. The DenseNet effectively extracts facial features by increasing the network depth, while the Unet preserves important facial details through skip connections. With improvements to the network structure and loss constraints, this method can minimize facial shape distortion and enrich facial detail information in near-infrared facial images. Although there has been some progress in converting infrared images to color images, the problem of semantic coding entanglement and geometric distortion remains unresolved. [PERSON] et al. [21] therefore proposed a top-down generative adversarial network called PearlGAN that aligns attention and gradient. By introducing attention-guided and loss modules, and adding a gradient alignment loss module, they reduced the ambiguity of semantic coding and improved the edge consistency of input-output images. However, due to an incomplete understanding of image features and limited information acquisition in existing methods, issues such as color leakage and loss of details still exist. [PERSON] et al. [22] introduced a multi-scale attention mechanism into the task of infrared and visible light image fusion, extracting attention maps from multi-scale features of both types of images and adding them to the GAN network to preserve more details. [PERSON] et al. [23] also addressed this issue by proposing a deep network for infrared and visible light image fusion, consisting of a feature learning module and a fusion learning mechanism, and designing an edge-guided attention mechanism on multi-scale features to guide attention to common structures during the fusion process. However, because the network is trained based on edge attention, during image reconstruction, the middle features tend to pay more attention to texture details, which makes it less effective when the source image contains a large amount of noise. Building upon the previous methods, this paper proposes an infrared image coloring algorithm based on the CGAN with a multi-scale feature fusion and attention mechanism. CGAN is an extension of GAN [24], which controls the image generation process by adding conditional constraints, resulting in better image quality and more detailed output. In this work, we improved the generator architecture of CGAN by incorporating a multi-scale convolution module with three types of convolutions in the U-Net network to fuse different scale features, enhance the network's feature extraction ability, improve learning speed and semantic understanding ability, and address issues such as color leakage and blurring during the coloring process. We added an attention module to the discriminator, which contains both channel attention and spatial attention, to filter the feature layers from a channel perspective and select important regions on the feature map. This allows the network to focus on useful features and ignore unnecessary ones, while also improving the discriminator's effectiveness and efficiency. By combining the improvements to the generator and discriminator, a new network with multi-scale feature fusion and attention module is obtained.