# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 1 Introduction  
Rain is a common weather condition that negatively impacts computer vision systems. Raindrops appear as bright streaks in images due to their high velocity and light scattering. Since image recognition and detection algorithms are designed for clean inputs, it is essential to develop an effective mechanism for rain streak removal. A number of research efforts have been reported in the literature focusing on restoring rain images, and different approaches have been taken. Some have attempted to remove rain streaks using video [1, 2, 3], while other researchers have focused on rain image recovery from a single image by considering the image as a signal separation task [4, 5, 6]. Since rain streaks overlap with background texture patterns, it is quite a challenging task to remove the rain streaks while maintaining the original texture in the background. Most of the times, this results in over-smoothed regions that are visible in the background after the de-raining process. De-raining algorithms [7, 8] tend to over de-rain or under de-rain the original image. A key limitation in the traditional, handcrafted methods is that the feature learning is manual and designed to deal only with certain types of rain streaks, and they do not perform well with varying scales, shapes, orientations, and densitiesof raindrops [9; 10]. In contrast, by using convolutional neural networks (CNNs), the feature learning process becomes an integral part of the algorithm and is able to unveil many hidden features. Convolutional neural network-based methods [11; 12; 13] have gained huge improvements in image de-raining during the last few years. These methods try to figure out a nonlinear mapping between the input rainy image and the expected ground truth image. Still, there is potential for improvements and optimizations within CNN-based image de-raining algorithms, which could lead to more visually appealing and accurate results. Instead of being just constrained to characterizing rain streaks, visual quality should also be considered when defining the optimization functions, which will result in improving the visual appeal of test results. When defining the objective function, it should consider the fact that the performance of vision algorithms, such as classification/detection, should not be affected by the presence of rain streaks. The addition of this discriminative information ensures that the output is indistinguishable from its original counterpart. Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the patterns in input data in such a way that the model can be used to generate new examples that are indistinguishable from reality. The concept of generative adversarial networks (GANs) was originally presented in [14] and has gained a high level of interest, with several successful applications and directions reported within a short period in the machine learning community. Existing CNN-based mechanisms only consider either \(L_{1}\)(Least Absolute Deviations) or \(L_{2}\) (Least Square Errors) errors, whereas in conditional GANs, they have additional adversarial loss components, which result in very good, qualitative, visually appealing image outputs. In our approach, we propose a conditional generative adversarial network-based framework for rain streak removal. Our model consists of a densely connected generator (G) network and a CNN-based discriminator (D) network. The generator network converts rainy images to de-rained images in such a way that it fools the discriminator network. In certain scenarios, traditional GANs tend to make output images more artificial and visually displeasing. To mitigate this issue, we have introduced a conditional CNN with skip connections for the generator. Skip connections guarantee better convergence by efficiently leveraging features from different layers of the network. The proposed model is based on the Pix2 Pix framework by [PERSON] et al.