Leveraging Scene Geometry and Depth Information for Robust Image Deraining

TL;DR

This paper presents a novel multi-network framework that leverages scene geometry and depth information to improve the robustness and accuracy of image deraining, especially for autonomous vehicle applications.

Contribution

It introduces a multi-network learning framework integrating depth information and feature consistency supervision for enhanced deraining performance.

Findings

Improved deraining quality over existing methods.

Enhanced object detection accuracy in rainy conditions.

Effective utilization of scene geometry and depth cues.

Abstract

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.