# R-PreNet: Deraining Network Based on Image Background Prior
## 2 Related Works
### Data-Driven Methods  
With the advancement of deep learning theories and techniques, data-driven single image deraining approaches are becoming increasingly prevalent. These methods automatically extract features from the dataset through network structures, thereby achieving mapping from rain images to deraining images. Since 2013, [PERSON] et al. [27] trained a special CNN by minimizing the mean square deviation between predicted rain and no rain image blocks, and for the first time, used deep learning methods to remove raindrops attached to images. To this day, numerous CNN-based deep networks for image deraining have been proposed. [13; 14; 28]. Usually, in these deep neural networks, constraints related to rainfall, such as rainfall masks and background features, are added to the network to learn features more comprehensively. Later, some methods utilized cyclic networks and residual networks [7] to gradually remove raindrops, which streamlined the network structure and reduced network parameters. However, due to the challenges of obtaining paired real rainy and rain-free images, there exists a disparity between synthesized rainy images and actual rainy photographs. Previous deraining algorithms may result in certain performance deviations when directly applied to real rain images. To address the above issues, experts have considered introducing unsupervised and semi-supervised methods in image deraining networks. Semi-supervised learning leverages both unlabeled and labeled data for training. For instance, [PERSON] et al. [24] introduced SIRR, a network that simulates genuine rainfall residuals through the likelihood term applied to the Gaussian mixture model, minimizing the Kullback-Leibler divergence between synthesized and real rain distributions. [PERSON] et al. [29] proposed Syn2 real (GP), which uses Gaussian process to model latent features of rainy images and generates pseudo labels for unlabeled data. [PERSON] et al. [30] proposed MOSS, which uses a memory oriented decoder encoder network to comprehend rain patterns and recover rain-free background images. By jointly mining rain streak features from both real and synthesized datasets through various approaches, these methods have enhanced the generalization capability of the deraining algorithms. Unsupervised learning means that it does not rely on the marked data and directly models the input data. The unsupervised algorithm in the deraining algorithm is implemented through the introduction of generative adversarial networks (GAN). [PERSON] et al. [31] proposed an unsupervised end-to-end adversarial deraining network termed RainRemoval GAN (RR-GAN), which is capable of generating genuine rain-free images solely using unpaired images. The network is chiefly comprised of a multi-scale attention memory generator and a multi-scale attention discriminator, with its architecture still bearing resemblance to supervised GAN methodologies. [PERSON] [32] proposed another unsupervised generative adversarial network (UD-GAN), which introduced self-supervision constraints into the internal statistical information of unpaired rain and clean images. It uses two mutually cooperative modules, namely the background guidance module (BGM) and the rain guidance module (RGM). The RGM is specifically designed to differentiate between genuine rain-free images and the fake rain-free images generated based on the BGM. The BGM ensures background consistency between the rain-streaked input and the down-sampled output by leveraging a hierarchical Gaussian blur gradient error. Afterwards, there were also unsupervised algorithms such as DerainCycleGAN [33] which solved the problems of difficulty in obtaining paired real rain images and rainless images, as well as poor generalization ability of algorithms based on synthetic images. However, there are still problems such as overly complex networks, time-consuming training, and insufficient rain pattern removal.