# Image Deraining Algorithm Based on Multi-Scale Features
## 2 Related Work
### Rain Removal Method Based on Supervised Learning
This method is different from previous multi-scale methods in that it explores and verifies internal scale connection methods to improve rain removal performance. [PERSON] et al. [30] proposed a framework called Multi-Scale Progressive Fusion Network (MSPFN) to eliminate rainlines in a single image. This method uses recursive calculations to capture global texture for similar rainlines at different locations. This allows complementary and redundant information to be explored in the spatial dimension to characterize the target rainline. At the same time, a multi-scale pyramid structure is constructed, and an attention mechanism is further introduced to guide the fine fusion of this relevant information from different scales. The method of collaboratively representing rainlines at different scales has inspired new research ideas in the field of rain removal. [PERSON] et al. [31] proposed an end-to-end detail recovery rain removal network, which introduced two parallel sub-networks with comprehensive loss functions, which cooperated to eliminate rainlines and restore the lost information caused by rainline removal. detail. The detail restoration branch of the detail restoration framework is also separable. This method divides and conquers the characteristics of different tasks and provides a new idea for parallel rain removal networks. [PERSON] et al. [32] proposed a new network architecture by setting the output residual of the network's inherent rain structure. The structural residual setting ensures that the rain layer extracted by the network accurately conforms to the general rainline test knowledge. Such a general regularization method will naturally achieve better accuracy and generalization capabilities. [PERSON] et al. [33] constructed a multi-level memory compensation network (MLMCN) for rain removal, which decomposes the learning task into several sub-problems. They designed a memory compensation block (MCB) to accurately estimate rainfall information and texture. The method works well on synthetic datasets and inspires research on processing multi-scale inputs with targeted modules. However, rainwater comes in a variety of forms and characteristics due to a number of factors such as shape, size, and wind speed. If the actual rain features do not match the model's preset ones, the effectiveness of the model will be affected. 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 there are still differences between synthetic rain maps compared to real rain maps. The generalization ability of the models 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. We propose a multi-scale feature-based image rain removal architecture, called MFD, which contains several key components. (1) A multi-scale feature extraction network is designed to enhance the sensory field of the model by extracting multi-scale feature information of rainwater images, and then effectively extract rainwater features of various sizes and orientations. (2) Use the channel attention module to focus on extracting feature information from each channel, then use the spatial attention module to enhance the extracted spatial information features. (3) Add horizontal connections between feature processing blocks to avoid any information loss.