Removing Snow From a Single Image Using a Residual Frequency Module and Perceptual RaLSGAN

Abstract

Removing snow particles from an image is a complicated task due to the particles’ shape, size, and color. The latest snow removal methods remove snow from a single image but retain some snow and salt-and-pepper particles. Some approaches, while trying to remove snow from a single image, produce blurry artifacts. In this paper, we solve these problems by designing a network model that consists of a residual generative network, a snow-free image generative network, and a perceptual relativistic discriminative network. In both generative networks, we assign the residual frequency network (ReFNet) as our bottleneck module. Our network model learns to map two relationships. First, the input snowy image is trained to map the snow mask image in the dataset. Then, a retained image resulting from subtraction between an input image and the estimated residual image is concatenated with the input snowy image and mapped to the desired snow-free ground truth. Moreover, we use a perceptual identical-paired adversarial network based on a relativistic discriminative network to make our training results more robust. Our results achieve greater performance than state-of-the-art methods on both synthetic and real-world snowy images.

Keywords

• Snow removal
• de-snow
• deep learning
• residual network
• convolutional neural network
• discrete wavelet transformation