## 1 Introduction
[6] proposed a very deep super-resolution convolutional network (VDSR), which uses 20 convolution layers to extract the features of the input image and introduces residual learning to ensure that the detailed features can be retained for a long distance. [PERSON] et al. [7] also proposed another deep recursive convolutional network (DRCN) to learn the differences in detail between HR images and LR images. Each layer of DRCN adopts a supervised recursive layer, which obtains high-frequency information by constantly cycling the recursive layer and uses a jump connection structure to reduce the disappearance of the gradient of the network. In order to improve the overall performance of the network, [PERSON] et al. [8] designed a deep Laplacian pyramid network for fast and accurate super resolution (LapSRN). This algorithm searches for multi-scale feature information by gradually up sampling the network and sharing the feature map and adopts the Laplacian pyramid structure to up sample layer by layer and fuse the residual image features, thereby obtaining a reconstructed super-resolution image. The model adopts the structure of step-by-step up sampling and does not need interpolation in the preprocessing stage, thus greatly reducing the complexity of network calculation. In order to make use of the channel information of the image, [PERSON] et al. [9] proposed a super-resolution algorithm based on channel attention networks (RCAN). This algorithm overcomes the disadvantage of existing convolutional neural networks by treating each channel feature equally and gives a certain attention weight to group each channel feature into new features. As a result, the attention mechanism can mine more channel and location information and draws a new research focus on super-resolution reconstruction network. However, these above-mentioned deep learning algorithms for image super-resolution reconstruction are generally aimed at natural images, and there is little research conducted on the super-resolution of infrared images, especially on the medium and long wave maritime infrared image. Infrared images have the disadvantages due to their low resolution, low contrast, and low signal-to-noise ratio. There are also problems such as little information given on edge texture, unclear gray level of images, poor noise suppression ability but much calculation of algorithms required to perform. Therefore, it is necessary and significant to research super-resolution reconstruction technology in the field of infrared imaging. In this research, we utilized new infrared optical materials and single-point diamond turning technology to design and process a medium and long wavelength infrared imaging optical system capable to provide a simple, light yet strong focusing ability using an equipped large relative aperture. The image results obtained from the system within the medium and long infrared spectrum are relatively clear [10]. We also proposed a super-resolution network model combining infrared and visible light images, which includes a feature extraction layer, an information extraction block and a reconstruction block. The model fuses the feature information extracted from visible light images and the infrared images and utilizes the important feature information on the edge and texture of the visible light image to compensate the detail of the infrared image and reconstructs the high-definition infrared image.