# Image Deraining Algorithm Based on Multi-Scale Features
## 1 Introduction  
In target detection, tracking, and recognition tasks, high-quality images can achieve better results. However, in actual scenarios, the visual effects and data quality of images are greatly reduced due to the influence of environmental factors, which affect subsequent detection, recognition, and other tasks. Therefore, clean background images and rain images need to be stripped from the original rain images. This is the image rain removal task. Rain streaks can cause severe occlusion of the background scene, or rain accumulation can significantly reduce the contrast and visibility of the scene. In practical application scenarios, although the quality of rain scene images is damaged, rich content can still be extracted from them, such as the target object of the image, edge information, spatial structure, text content, semantic emotional information, etc. [1]. The innovation of new technologies has brought about ever-changing needs, which in turn has promoted the development of new technologies. The two complement each other. From research that ignores weather factors to in-depth exploration of image restoration technology, such as image rain removal, this phenomenon is favorable proof of the iterative development of image processing technology. Therefore, how to apply computer vision-related algorithms to efficiently process rain scene image data has become a difficultythat needs to be overcome in the field of computer vision, and it is also the only way for computer vision to become practical and popular [2]. Early image rain removal methods used loss functions and various prior knowledge to represent the characteristics of the rain layer and background layer [2; 3; 4; 5; 6]. Since 2017, image rain removal methods have entered the data-driven stage. In the era of deep learning, various deep learning neural networks are used to accurately extract the required rain map features to complete rain removal, such as convolutional neural network (CNN) [7; 8; 9], recurrent neural network (RNN) [10; 11], Generative Adversarial Network (GAN) [12; 13; 14], semi/unsupervised learning [15; 16; 17], etc., these methods have demonstrated excellent performance. However, the biggest problem currently encountered by data-driven deep-learning rain removal methods is that the trained network generally has poor rain removal performance and weak generalization performance in real scenarios. First of all, existing research does not have a completely real training dataset because it cannot capture images with both rain and no rain at the same time. Therefore, the images and scene information of the same scene at different times are also different and cannot be used as a perfect comparison reference. Secondly, the current synthetic rain dataset is generally based on the physical modeling of rain, and it is difficult for the physical model to take into account all rain characteristics. Therefore, the training dataset has a certain bias, which leads to the problem of rain removal based on deep learning. The performance of the method will be greatly reduced or even useless. Current image deraining techniques face several challenges:  
1. Diversity of rain. Influenced by a variety of factors such as shape, size, and wind speed, rainwater has a variety of forms and characteristics. If the actual rain features are not consistent with the model presets, the effectiveness of this model will be affected. 2. Pairs of images with and without rain are difficult to obtain. Existing models are effective on synthetic datasets, but it is difficult to take paired no-rain and rain images in the real world, and the synthetic rain images are still different compared to the real rain images. 3. The generalization ability of the model needs to be improved. Currently, synthetic rain images are mainly used to train and evaluate the model, however, there are differences between synthetic rain images and real rain images. This may not be effective when dealing with irregular or dense rain in natural environments.