Single Image De-Raining via Generative Adversarial Nets

Abstract

In this paper, we propose a Generative Adversarial Network for Single Image De-raining(GAN-SID). We observe that batch normalization has side effects in the de-raining task. Therefore, we introduce instance normalization to replace the traditional batch normalization layers in both generator and discriminator. Motivated by the Squeeze-and-Excitation (SE) network that can learn the importance of channels, we introduce SE module in the generator to give different weights to the learned features. To preserve image details while removing rain streaks, we propose to utilize pixel-wise loss, perceptual loss, and adversarial loss to train the proposed network. Experiments on two synthetic datasets and real world images demonstrate that the proposed method outperforms state-of-the-art de-raining works in both objective and subjective measurements.