# Image Deraining Algorithm Based on Multi-Scale Features
## 2 Related Work
### Rain Removal Method Based on Supervised Learning  
As early as 2013, [PERSON] [21] et al. built a three-layer convolutional neural network to learn the mapping between damaged image patches and clear image patches. Although its network structure is simple, it provides inspiration for subsequent image rain removal algorithms based on deep learning methods. In view of the rapid development of deep learning technology, some scholars have begun to apply deep learning to the research of image rain removal algorithms. Since 2017, image rain removal technology based on deep learning (mainly supervised learning) has sprung up. In 2017, [PERSON] et al. [22] built a multi-task architecture to learn binary rainline images, rain appearance, and clear background images. In 2017, [PERSON] et al. [23, 24] designed a deep detail network taking high-frequency details as input to predict the residual between the input image and the real image. It is worth noting that the input of high-frequency details is an important exploration of the combination of deep learning and prior knowledge. However, a key problem with early methods based on deep learning is the poor rain removal performance of the network. With the emergence of various variants of convolutional neural networks, research on image rain removal algorithms based on convolutional neural networks has become increasingly mature. [PERSON] et al. [25] built an autoencoder network with non-locally enhanced dense blocks, where the weight values of the non-local feature maps change following four densely connected convolutional layers. [PERSON] et al. [26] use the residuals generated by shallow image blocks to guide deep image blocks, predict negative residuals from coarse to fine, and finally fuse the outputs of different blocks. This method is an important attempt to achieve multi-scale feature fusion in the field of rain removal from a single image. Shallow features guide deep features to achieve rain removal operations from coarse to fine. An important part of supervised learning is labeled datasets. Image rain removal methods based on supervised learning mostly use synthetic datasets. Therefore, the synthesis of rainwater datasets has also attracted the interest of researchers. [PERSON] et al. [27] created a RainCityscapes dataset related to scene depth. At the same time, they proposed an attention mechanism based on depth guidance and built a deep network to predict residual images; coincidentally, [PERSON] et al. [28] also constructed a high-quality dataset, including 29,500 pairs of real rain images and pseudo-real rain images. Then, they designed a new spatial attention network to effectively learn rain removal discrimination from local to global features. The creation of new datasets provides a basic data guarantee for subsequent research on rain removal algorithms and creates new possibilities for the development of rain removal algorithms. Research on image rain removal algorithms based on convolutional neural networks is no longer satisfied with a single scale and network architecture and has begun to integrate multi-scale content and multi-layer networks. The network proposed by [PERSON] et al. [11] combines the methods of Gaussian-Laplacian image pyramid decomposition and deep neural networks, using recursive and residual network structures to aggregate features of different layers. The introduction of the Gaussian-Laplacian image pyramid in this method not only realizes the lightweight of the rain removal network but also fully extracts the features of the multi-scale rain image to complete the subsequent rain removal operation. It is a combination of prior knowledge and knowledge in the field of single-image rain removal. Another successful case of the integration of supervised learning methods. [PERSON] et al. [29] proposed a multi-subnetwork structure that can learn features at different scales. These subnetworks are integrated into internal learning in a cross-scale manner by gated recurrent units and make full use of these subnetworks at different scales.