# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 1 Introduction  
In outdoor photography, the quality of images is often susceptible to the influence of different weather conditions. Rainy weather is common and can influence high-level tasks in computer vision, such as object detection [1] and the operation of intelligent vehicles [2]. To improve performance in these tasks, algorithms for removing artifacts and restoring background information in distorted images must be developed. However, developing such algorithms is challenging because of the uncertainty of the background information in distorted images. Over the past few decades, several studies have attempted to address the aforementioned problems, and an extensive range of techniques have been proposed for rain removal in images. For example, rain removal from a single image can be realized using various wave filters such as bilateral filters [3] and guided filters [4] to decompose the rainy image into high-frequency and low-frequency parts and then recover a rain-free image by combining the feature selection of the low-frequency and high-frequency parts. Recently, various methods have been proposed for rain streak removal through theoretical analyses of the physical properties of the rain and background layers. The representative methods that involve this approach include those using layer priors based on the Gaussian mixture model (GMM) [5], discriminative sparse coding (DSC) [6], joint convolutional analysis (JCAS), and synthesis sparse representation (SSR) [7]. However, images with complex rain shapes and background scenes cannot be flexibly processed using techniques based on prior analytical models. The recent success of deep learning methods in computer vision has encouraged researchers to explore approaches to rain removal in images. Instead of manually inputting information regarding the properties and features of rain-affected images, deep-learning-based methods leverage sophisticated model architectures, data-rich training samples, and non-linear representation modeling. The representative approaches have been RESCAN [7], PReNet [8], LPNet [9], MSPFN [10], MPRNet [11], VRGNet [12], PCNet [13], and RAiA-Net [14]. Deep-learning-based methods are more robust and have quick developmentcycles compared with traditional-algorithm-based methods [3; 4; 5; 6]. However, deep learning models are hampered by the vanishing and exploding gradient problem because of their deep architecture. A few weaknesses and areas of improvement based on recent studies are listed as follows: the receptive field size of a convolutional kernel is essential for modeling visual patterns. However, using a sizeable convolutional kernel throughout the model is not feasible and incurs a large computational and memory expense. A few studies on computer vision tasks [15; 16] have demonstrated that multi-scale feature encoding could effectively help extract features on different scales. After our analysis, none of these methods [8; 9; 10; 11; 12; 13; 14] used multi-scale kernels for rain removal, resulting in poor rain removal performance. Furthermore, the loss function in several methods [7; 13] is based on pixel-level differences; thus, the trained models are not focused on structural representation. The overall objective of this dissertation is to develop a novel deep learning method to solve image deraining. The organization of each section in this dissertation are as follows. In Section 2, the related work of rain removal is briefly introduced, including on video deraining methods and single image deraining methods. A multi-scale aggregation residual channel attention fusion network (MARCAFNet) is proposed in Section 3. The experimental results are demonstrated in Section 4. Finally, brief concluding remarks and prospects for future works are presented in Section 5. To address the aforementioned problems, this thesis proposes a deep learning architecture that accounts for the different scales of rain streaks and aids the recovery of the affected images to obtain images with satisfactory quality.