The superior performance introduced by deep learning approaches in removing atmospheric particles such as snow and rain from a single image; favors their usage over classical ones. However, deep learning-based approaches still suffer from challenges related to the particle appearance characteristics such as size, type, and transparency. Furthermore, due to the unique characteristics of rain and snow particles, single network based deep learning approaches struggle in handling both degradation scenarios simultaneously. In this paper, a global framework that consists of two Generative Adversarial Networks (GANs) is proposed where each handles the removal of each particle individually. The architectures of both desnowing and deraining GANs introduce the integration of a feature extraction phase with the classical U-net generator network which in turn enhances the removal performance in the presence of severe variations in size and appearance. Furthermore, a realistic dataset that contains pairs of snowy images next to their groundtruth images estimated using a low-rank approximation approach; is presented. The experiments show that the proposed desnowing and deraining approaches achieve significant improvements in comparison to the state-of-the-art approaches when tested on both synthetic and realistic datasets.

翻译：深度学习方法在从单幅图像中去除雪、雨等大气颗粒物方面展现出的优越性能，使其相较于传统方法更具优势。然而，基于深度学习的方法仍面临颗粒物外观特征（如尺寸、类型和透明度）相关的挑战。此外，由于雨雪颗粒的独特特性，基于单一网络的深度学习方法难以同时处理这两种退化场景。本文提出了一种由两个生成对抗网络（GAN）组成的全局框架，每个网络分别处理一种颗粒物的去除。去雪和去雨GAN的架构均引入了特征提取阶段与经典U-net生成器网络的集成，从而在颗粒尺寸和外观存在剧烈变化时增强了去除性能。此外，本文还提出了一个真实数据集，其中包含雪景图像及其通过低秩近似方法估计的真实背景图像对。实验表明，在合成数据集和真实数据集上的测试结果中，所提出的去雪和去雨方法相较于现有先进方法均取得了显著提升。