## 1 Introduction  
Adverse weather conditions such as rain, snow, and haze can alter the contents and colors of digital images and thus degrade their visual quality [1, 2], which will hamper the performance of visual perception systems of unmanned surface vessels (USV), such as object detection, segmentation, tracking, surveillance [3], and driving assistance systems [4]. However, these algorithms are developed under clear weather conditions [5]. In particular, rain streaks usually have multiple directions and present different intensities in digital images [1]. Large water particles from the rain streaks randomly occlude the scene, resulting in a locally dense negative impact on the image [6]. It is formulated as:  
\[D=I+R, \tag{1}\]  
where rain image \(D\) can be expressed using a linear combination of rain-free background image \(I\) and rain streak \(R\). The ultimate goal of deraining is to recover clean image \(I\) by removing rain streak \(R\) from \(D\). Obviously, image deraining is a highly ill-posed problem [7], because we cannot find the unique solution of \(I\) from the given \(D\)[6]. In addition, fine particles generated from the rain streaks distort and blur the image. Hence, it is an essential and challenging task to remove the rain streaks from the image so vision systems will be robust to the maritime environment [2]. Early methods are mostly based on subjective prior knowledge and the linear characteristic of the rainy images [1; 8; 9; 10] by estimating the rain streaks in the image. Prior information about the background image and rain streaks are designed to constrain the solution space [11]. A single image deraining algorithm based on the generation of a raindrop nonlinear physical model is designed in [12], which can accurately separate the extracted image layer and rain layer. However, this method also shows some limitations, such as some artifacts around the area containing rain streaks, incomplete raindrop removal and others. The deraining process is regarded as a process of dividing the image into a rainless layer and a rain layer for the sparse coding dictionary learning-based method. The rain image is decomposed into low-frequency and high-frequency parts by using bilateral filters in [13], and then the high-frequency part will be decomposed into rain components and non-rain components through sparse coding and dictionary learning, so that rain streaks can be successfully removed from the image. However, the deraining algorithm based on sparse coding dictionary learning has the problem of blurring some details of the original image in the process of removing rain streaks. In summary, these prior-based methods always behave ineffectively in real scenes, especially for highly diverse rain removal, because prior knowledge is difficult to obtain precisely. Besides, the prior-based method can be regarded as the optimization of the cost function, and it usually requires a high time cost [7]. Thus, it cannot be easily matched to the design requirements for mobile and embedded vision applications [7]. Recently, with the rise of deep learning and its computing performance, supervised learning-based convolutional neural network (CNN) algorithms have emerged with promising performance through designing various network structures with unique nonlinear mapping transformation [14; 15; 16; 17]. These works can automatically extract features of the rain layer and separate them from the original image to get a clear background [7]. The network-based approach is data-driven, the quality of the approach is determined by data, rain model, and network architecture [11]. The popular deep networks are generally constructed with complicated branches and numerous layers to complete different deraining tasks for further performance improvement. The LPNet deraining network designed in [18] is based on fully supervised learning; a Gaussian pyramid is combined to improve the deraining effect and make the model lightweight. The network is trained with synthetic datasets and achieved a good deraining performance.