Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

TL;DR

This paper introduces ADU-Net, a novel neural network architecture that jointly removes rain and haze from images while preserving scene details, significantly outperforming existing methods on multiple benchmarks.

Contribution

The paper proposes a new asymmetric dual-decoder U-Net that simultaneously estimates contamination and scene residuals for effective rain and haze removal.

Findings

Outperforms state-of-the-art methods on synthetic and real-world benchmarks.

Improves PSNR by 2.26 and 4.57 on RainCityscapes and SPA-Data datasets.

Effectively preserves scene information while removing weather artifacts.

Abstract

This work studies the joint rain and haze removal problem. In real-life scenarios, rain and haze, two often co-occurring common weather phenomena, can greatly degrade the clarity and quality of the scene images, leading to a performance drop in the visual applications, such as autonomous driving. However, jointly removing the rain and haze in scene images is ill-posed and challenging, where the existence of haze and rain and the change of atmosphere light, can both degrade the scene information. Current methods focus on the contamination removal part, thus ignoring the restoration of the scene information affected by the change of atmospheric light. We propose a novel deep neural network, named Asymmetric Dual-decoder U-Net (ADU-Net), to address the aforementioned challenge. The ADU-Net produces both the contamination residual and the scene residual to efficiently remove the rain and haze while preserving the fidelity of the scene information. Extensive experiments show our work outperforms the existing state-of-the-art methods by a considerable margin in both synthetic data and real-world data benchmarks, including RainCityscapes, BID Rain, and SPA-Data. For instance, we improve the state-of-the-art PSNR value by 2.26/4.57 on the RainCityscapes/SPA-Data, respectively. Codes will be made available freely to the research community.