利用深度信息提升雨天图像去雨效果，增强自动驾驶视觉可靠性
Leveraging Scene Geometry and Depth Information for Robust Image Deraining
- 设计多网络框架，融合深度图与场景几何先验
- 在三个数据集上显著提升去雨质量，改善目标检测性能
- 适合自动驾驶、智能视觉系统开发者参考
图像去雨对提升自动驾驶在雨天环境下的视觉感知能力具有重要意义，有助于实现更安全的驾驶。以往方法多依赖单一网络结构生成去雨图像，但未能充分挖掘场景中蕴含的丰富先验知识。尤其多数方法忽略了能提供场景几何上下文的深度信息，从而影响去雨的鲁棒性。本文提出一种新型学习框架，包含一个用于去雨的自编码器、一个引入深度信息的辅助网络，以及两个监督网络以强制雨天与晴天场景间的特征一致性。该多网络设计使模型能有效捕捉底层场景结构，生成更清晰、更准确的去雨图像，进而提升自动驾驶中的目标检测表现。在三个常用数据集上的大量实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Image deraining holds great potential for enhancing the vision of autonomous vehicles in rainy conditions, contributing to safer driving. Previous works have primarily focused on employing a single network architecture to generate derained images. However, they often fail to fully exploit the rich prior knowledge embedded in the scenes. Particularly, most methods overlook the depth information that can provide valuable context about scene geometry and guide more robust deraining. In this work, we introduce a novel learning framework that integrates multiple networks: an AutoEncoder for deraining, an auxiliary network to incorporate depth information, and two supervision networks to enforce feature consistency between rainy and clear scenes. This multi-network design enables our model to effectively capture the underlying scene structure, producing clearer and more accurately derained images, leading to improved object detection for autonomous vehicles. Extensive experiments on three widely-used datasets demonstrated the effectiveness of our proposed method.
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