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.
Highlights
BAD weather influences outdoor vision systems, such as security or surveillance cameras, auto-driving systems, and traffic monitors
Though some of the learning-based methods mentioned above can be considered as generic methods for removing atmospheric particles, Liu et al [19] claimed that it is difficult to adapt those methods to snow removal due to the complications of snow characteristics, including uneven density, diverse particle shapes and sizes, transparency, and irregular trajectory
We propose a bottleneck module, which we refer to as the residual frequency network (ReFNet)
Summary
BAD weather influences outdoor vision systems, such as security or surveillance cameras, auto-driving systems, and traffic monitors. Though some of the learning-based methods mentioned above can be considered as generic methods for removing atmospheric particles, Liu et al [19] claimed that it is difficult to adapt those methods to snow removal due to the complications of snow characteristics, including uneven density, diverse particle shapes and sizes, transparency, and irregular trajectory They introduced a synthetic Snow100K2 dataset consisting of 100K synthesized snowy images, corresponding snow-free ground truths, snow masks, and an additional 1,329 real-world snowy images. Li et al [20] used the same Snow100K2 dataset to train their model They proposed composition generative adversarial networks (CGANs) to remove snowflakes from a single image with different sizes of snow particles.
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