## 1 Introduction  
Due to challenging weather and difficulties in monitoring, maritime emergencies and marine disasters usually occur in the coastal waters around the globe, causing serious harm to the development of marine economy. In order to achieve a cost-effective solution for intelligent ocean observation and detection in a complex background, many factors should be taken into consideration, such as \"real-time\", \"clear\" and \"low-cost\" etc. It is urgent to research new imaging mechanisms to achieve multi-dimensional and high-resolution monitoring of Marine Targets under complex sea conditions [1]. With the rapid development of science and technology, infrared imaging technology has received high attention worldwide, particularly when involving with the medium and long wavelength bandsthat are utilized for wide range of practical applications due to their extraordinarily strong radiation ability. The medium and long wave dual band infrared imaging technology can simultaneously obtain the infrared radiation information on two atmospheric windows of the medium and long wave, and thus obtain more effective information of the target for improving the adaptability of the photoelectric system under various complex environmental conditions. Practically, it has wide and important applications in coastal monitoring military, medicine and other fields [2]. Compared with the visible light imaging system, the reliability and stability of the current infrared imaging system still have drawbacks as its performance can be greatly affected by the environment. For example, Gaussian noise caused by thermal noise, temperature noise and amplifier noise of the detector during signal acquisition, optical blur caused by optical components, etc., can all affect the quality of infrared image. However, with the continuous development of image display technology, it is entirely possible that infrared technology can also achieve high image quality. To meet the urgent demand from the industry side, researchers have never stopped searching for better ways that enhance the quality of infrared images and improve the resolution of infrared images. At the beginning, the super-resolution reconstruction method based on machine learning establishes a high-resolution (HR) and a low-resolution (LR) database for categorizing images in pairs, and machine training and learning are then carried out to establish a mapping relationship between the two images. For the LR images of each input target, the HR images of each output target can be directly obtained using a trained mapping relationship between HR and LR [3]. As new methods continuous to evolve, image super-resolution reconstruction based on deep learning has become a research hotspot. [PERSON] et al. [4] first proposed a convolutional neural network for super resolution (SRCNN) in 2014. It can iteratively optimize all reconstruction processes except interpolation preprocessing, for reasonably good reconstruction results. In order to quickly and effectively realize super-resolution models at different scales, [PERSON] et al. [5] proposed an accelerated version of neural network super-resolution model (FSRCNN), which uses deconvolution operation to up sample images at the last layer of the network, and changes the dimension of intermediate features by adding mapping layers and reducing convolution kernels, and sharing intermediate mapping layer parameters. When training super-resolution models at different scales, it is only necessary to fine tune the parameters of the deconvolution layer, and thus the training complexity can be reduced. Although the increase of convolution layer is conducive to mine useful detail information, the feature image generated by continuous operation at the convolution layer will become smaller and smaller, and the loss of image gradient will also increase. In order to reduce the gradient loss caused by this network deepening, in 2016, [PERSON] et al.