Single Image De-raining Based on a Novel Enhanced Attentive Generative Adversarial Network

Abstract

With rapid development of deep learning in artificial intelligence and computer vision, generative adversarial network plays an important role in single image de-raining. Attentive generative adversarial network (AttGAN) has problems about complicated network structure and distortion of background color. Considering the relative complex background in the real image with raindrops, a new single image de-raining algorithm based on enhanced attentive generative adversarial network (EAttGAN) is proposed to retain the original background of the blurred image. In order to restore more complete background and accelerate network training, a generator enhanced by residual scaling and a Markovian discriminator are fused effectively in the network. Compared with AttGAN, experimental results indicate that EAttGAN can not only achieve higher sharpness of a single image, but also take less time in the training process.