Abstract: Raindrops seriously affect the visual effect of images and subsequent image processing applications.At pre-sent,the single image raindrop removal method based on deep learning can effectively mining depth features of image,so its effect of removing rain is better than traditional methods.However,with the increasing of network depth,overfitting is easy to occur,resulting in the bottleneck of rain removal effect.This paper proposed a novel single image raindrop removal algorithm based on deep learning.Firstly,on the basis of inheriting the advantages of deep learning,network learns the residuals between rain images and no-rain images.Secondly,the raindrop-removed image is reconstructed from the residual image and the source image.Through these steps,the depth of network is increased and the convergence speed is accelerated.In terms of performance evaluations,a dataset consisting of images in various scenes was used to test the proposed method,and the results were also compared with those of the state-of-the-art raindrop removal methods.The experimental results show the superiority of the proposed algorithm.

Key words: Convolutional neural network, Deep learning, Raindrop removal