Single image rain removal using cWGAN network

The atmospheric conditions like rain degrade visibility, creating problems for computer vision applications. The early rain removal works are video-based; hence, they have temporal information, making the rain removal work much easier. In single image de-raining, the lack of temporal information creates challenges. Deep learning-based networks are recently popular for single-image rain removal. The networks may or may not use image decomposition for rain removal. This paper presents an end-to-end Wasserstein Generative Adversarial Network (WGAN) to restore a rain-free image from a rain image that does not require image decomposition. The network is trained using a combined Wasserstein, the mean absolute error or L1 loss, and VGG loss (perceptual loss) to improve the quality of generated rain-free images. Two networks, U-Net and W-Net, are trained as generators to show the network's performance. The proposed cWGAN is an end-to-end network that does not require further enhancement. An extensive test using natural and synthetic rainy images reveals that the proposed cWGAN network competes against the recent single image de-raining techniques.