Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

Abstract

We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.

Existing System

Discriminative Sparse Coding, Shepard Convolutional Neural Networks.

Proposed System

We have presented a deep learning architecture called DerainNet for removing rain from individual images. Using a convolutional neural network on the high frequency detail content, our approach learns the mapping function between clean and rainy image detail layers. Since we do not possess the ground truth clean images corresponding to real-world rainy images, we synthesize clean/rainy image pairs for network learning, and showed how this network still transfers well to real-world images. We showed that deep learning with convolutional neural networks, a technology widely used for high level vision task, can also be exploited to successfully deal with natural images under bad weather conditions. We also showed that DerainNet noticeably outperforms other state-of-the-art methods with respect to image quality and computational efficiency.

Architecture

BLOCK DIAGRAM