# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 2 Related Work
### Single Image Deraining Methods  
Image processing and machine learning were the early methods used for the removal of rain from a single image [3; 4; 5; 6; 27; 28]. [PERSON] et al. [3] used bilateral filters to decompose high and low frequencies, and then decomposed the high-frequency parts into rain and non-rain components. Finally, they used a sparse coding dictionary to learn and recover the rain-affected areas. [PERSON] et al. [27] proposed an unsupervised model using a priori knowledge of images to distinguish rain streaks from other high-frequency details. [PERSON] et al. [6] employed discriminative encoding in learning a dictionary for use in separating the background and rain streaks using the coefficient vector sparsity of rain streaks. [PERSON] et al. [5] explored the use of hybrid model GMMs, including GMMs of the background layer learned from natural images. The scale and directions of rain streaks are learned from images to enable removal from the background layer and rain layer. [PERSON] et al. [4] used guided filters to decompose input images into high-frequency and low-frequency parts to facilitate the extraction of image details from high-frequency components and subsequent rain removal. [PERSON] et al. [28] decomposed a single input image into a rain-free background layer and a rain streak layer. They then analyzed the local gradient of the rain streak layer as prior knowledge for rain streak removal. These methods proved effective in the rain streak removal and for reducing the memory requirements for deraining. However, with the development of technology, image resolution quality is a growing concern and further advances are required to enhance image quality. In recent years, with the rapid development of deep learning, a number of researchers have reported that deep learning methods outperform image processing and machine learning methods [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. [PERSON] et al. [29] proposed a deep-learning-based correlation module that used photos taken through a window as training data to guide the mapping of noisy image patches against clean image patches for the removal of static rain streaks and dirt artifacts. [PERSON] et al. [30; 31] used a convolution neural network to map the relationship between rain streak images and rain-free images for rain removal via residual learning and image parsing. [PERSON] et al. [7] combined a convolutional network and a recurrent neural network to enhance rain removal performance. The method obtained image features using an extended convolutional network and retained useful information from previous stages via a recurrent neural network to further improve rain removal performance. [PERSON] et al. [32] analyzed the visual effects of rain on images according to scene depth and then used attention mechanisms to learn deep features for use in rain streak removal in an end-to-end neural network. [PERSON] et al. [33] built a large dataset of rain/no rain pairs and developed a semi-supervised model to derive local-to-global spatial attention features for rain streak removal. [PERSON] et al. [8] proposed the ResNet model that removed rain streaks from pictures by reusing shallow ResNet modules in conjunction with feature stacking between modules of different phases for rain removal. [PERSON] et al. [9] proposed a lightweight pyramid module (LPNet) using the Gauss-Laplacian decomposition technique, which greatly reduced the parameter problem in deraining images. [PERSON] et al. [10] proposed an end-to-end multi-scale progressive fusion network that used recurrent computation to capture global details, explore information in spatial dimensions, and cooperate with attention mechanisms to facilitate image fusion on various scales. [PERSON] et al. [34] proposed a conditional generative adversarial network, which produced global and local image information through the generator and used the discriminator to identify true and false. [PERSON] et al.