针对雨雪图像去噪，提出双GAN联合方案，效果优于现有方法。
End-to-end Inception-Unet based Generative Adversarial Networks for Snow and Rain Removals
- 采用双GAN架构，分别处理雨和雪的去除任务，提升针对性。
- 引入改进的Inception-Unet结构，增强对大小与透明度变化的适应能力。
- 构建真实雨雪配对数据集，实验验证在合成与真实数据上均领先。
深度学习在单张图像去除雪、雨等大气颗粒方面表现优异，但面对颗粒尺寸、类型和透明度差异仍存在挑战。由于雨雪特性不同，单一网络难以同时有效处理两种退化。本文提出一种双生成对抗网络（GAN）全局框架，每个GAN独立负责一种颗粒去除。两个去雪和去雨GAN均结合Inception模块与经典U-net生成器，强化特征提取能力，显著提升在严重尺寸与外观变化下的去噪性能。此外，构建了一个真实数据集，通过低秩近似估计真实无雪图像作为标签。实验表明，该方法在合成与真实数据集上均显著优于当前最优技术。
原文摘要 · Abstract (English)
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.
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