Attentive Generative Adversarial Network for Raindrop Removal
from A Single Image
Abstract
Raindrops adhered to a glass window or camera lens can
severely hamper the visibility of a background scene and
degrade an image considerably. In this paper, we address
the problem by visually removing raindrops, and thus transforming a raindrop degraded image into a clean one. The
problem is intractable, since first the regions occluded by
raindrops are not given. Second, the information about the
background scene of the occluded regions is completely lost
for most part. To resolve the problem, we apply an attentive
generative network using adversarial training. Our main
idea is to inject visual attention into both the generative and
discriminative networks. During the training, our visual attention learns about raindrop regions and their surroundings. Hence, by injecting this information, the generative
network will pay more attention to the raindrop regions and
the surrounding structures, and the discriminative network
will be able to assess the local consistency of the restored
regions. This injection of visual attention to both generative and discriminative networks is the main contribution of
this paper. Our experiments show the effectiveness of our
approach, which outperforms the state of the art methods
quantitatively and qualitatively