## 1 Introduction  
Near-infrared (NIR) images obtained by an active imaging system have the advantages of good concealment, high resolution, and rich detail information. Therefore, NIR images begin to be widely used in some emerging fields of machine vision, such as night-vision-assisted driving systems [1], night video surveillance [2], and real-time wildlife monitoring [3]. The NIR image is a single channel grayscale image that will reduce the user's visual sensory acceptance and cause difficulty in target recognition [4]. Therefore, it is meaningful to convert grayscale NIR images into multichannel RGB images in terms of user application and visualization. It was once generally considered that the task of colorization of NIR images was similar to that of grayscale images, since the two types of images are single-channel gray images. However, some scholars' research [5, 6] indicated that the colorization methods of grayscale image are apparently not suitable for colorizing NIR images. The main reason is that the spectral range of grayscale images is different to that of NIR images. Grayscale images and RGB images are located in the same spectral range. There is an obvious correlation between grayscale images and the luminance of RGB images. Therefore, grayscale colorization can recover color well by using the grayscale image as the luminance information and only estimating the chroma map. Conversely, the spectral band of NIR images exceeds the spectral range of the visible wavelength and an NIR image is linearly independent of the luminance of an RGB image. When using an NIR image as the luminance information, it leads to a phenomenon that the NIR image is \"transparent\" to a number of colorants or paints [7]. Therefore, the NIR image colorization task has greater complexity and ambiguity since it needs to establish a single-channel to three-channel mapping [8]. In recent years, NIR colorization methods based on deep learning network have received ever-increasing attention. Symmetric codecs are widely used as the main network architecture in both CNN-based colorization networks and CycleGAN-based colorization networks. However, some scholars pointed out that the standard symmetric UNet [9] is considered insufficient to simulate the high-level semantic work of NIR colorization [10]. Although the asymmetric codec has not been used in the field of NIR colorization, it has been successfully applied in image segmentation [11]. It is composed of UNet and ResNet [12] to improve segmentation accuracy without any significant increase in number of parameters. Later, D-Linknet [13] was proposed by adding dilated convolution layers between the encoder and decoder of LinkNet. Therefore, this paper proposes a novel NIR colorization method using asymmetric codec (ACD) and pixel-level fusion. In ACD, we have one less downsampling layer than ResNet [12], which is enough to guarantee the effect of sampling, and bilinear interpolation is used for upsampling instead of deconvolution in LinkNet. In addition, a global-local feature fusion network (GLFFNet) is embedded between the encoder and decoder. The ACD and GLFFNet together constitute the colorization network (ColorNet) in this paper. To improve the final colorization result, bilateral filtering and weighted least squares filtering (BFWLS) are combined to fuse the pixel-level information of the input NIR image into the raw output image of the ColorNet. The main contributions of this paper are as follows: (1) an ACD is designed in the ColorNet to improve the feature extraction ability of the encoder by allowing the information to enter deeper into the model and learning more non-redundant information; (2) a GLFFNet is built between the encoder and the decoder to reduce the information loss in the pooling layer of the encoder and improve the prediction of the subtle color of the image [14]; (3) using BFWLS, the input NIR image is fused with the luminance of the output image derived by the decoder to enhance the details of the final output image.