Single Image Snow Removal via Composition Generative Adversarial Networks

Abstract

Snowflakes attached to the camera lens can severely affect the visibility of the background scene and compromise the image quality. In this paper, we solve this problem by visually removing snowflakes to convert the snowy image into a clean one. The problem is troublesome; the information about the background of the occluded regions is completely lost for the most part. For removing snowflakes from a single image, we proposed a composition generative adversarial network. Different from the previous generative adversarial networks, our generator network comprises clean background module and a snow mask estimate module. The clean background module aims to generate a clear image from an input snowy image, and snow mask estimate module is used to produce the snow mask in an input image. During the training step, we put forward a composition loss between the input snowy image and composition of the generated clean image and estimated snow mask. We use a dataset named Snow100K2 including indoor and outdoor scenes to train and test the proposed method. The extensive experiments on both synthetic and real-world images show that our network has a good effect and it is superior to the other state-of-the-art methods.