Application research on improved CGAN in image raindrop removal

Abstract

Rainy weather can greatly reduce the image quality and hinder the subsequent processing of the image. In order to achieve raindrop removal on rainy images, the single image raindrop removal method based on conditional generative adversarial networks (CGAN) is proposed. In this method, CGAN is used as the basic framework. The network receives the raindrop image as an additional condition information and adds Lipschitz constraint on the network. The network model is trained by the combination of condition adversarial loss, content loss, and perception loss to repair the raindrop area and reconstruct the image. The experimental results show that the proposed method has better raindrop removal effect than the existing algorithm and can avoid image blurring on the basis of ensuring the raindrop removal effect.