Abstract: In order to remove raindrops from images taken on rainy days, aiming at the issues that the area covered by raindrops is unknown, most of the background information in the raindrop area has been lost, and the image clarity and global information attention are needed to improve, a self-attention layer is added to the self-encoding structure, and a multi-scale discriminator is introduced into the discriminant network. Guided by the attention distribution map, the optimization of the self-attention layer and the evaluation of the multi-scale discriminator, the generating network considers the global information more under the premise of paying attention to the raindrop area. The multi-scale discriminator can better distinguish the gap between the raindrop image and the clear image from coarsely to finely. The experiment completed the comparison between the proposed method and other methods, the self- comparison, and the evaluation with the peak signal-to-noise ratio and structural similarity, which proves that the proposed method is effective and its quality and index values are higher than other methods.

Key words: raindrop removal, generative adversarial network, self-attention, multi-scale