Abstract:
Visual quality of rainy images are considerably poor due to the raindrops in camera
lens and the rain streaks in the background scenes. Although the raindrops and rain streaks are
appeared together in real-world rainy images, most of the previous approaches are proposed to
remove either of them. In this paper, we have proposed a novel CNN model architecture to remove
raindrops and rain streaks together. The proposed CNN model architecture has two branches and
it consumes two formats of a rainy image via an encoder-decoder network and a dense CNN
network. At the end of the architecture, outputs of both branches are combined to produce a highvisibility
rain free image with natural colors. In addition, internal and external skip connections
are introduced in the blocks of these branches to improve the performance further. The proposed
model is trained and then tested on Raindrop, Rain100H, Rain100L, and Rain12 benchmarks and
showed excellent performance than the state-of-the-art approaches.
