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arXiv · 2603.16446

Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline

Xingyu Liu·Zewei He·Yu Chen·Chunyu Zhu·Zixuan Chen·Xing Luo·Zhe-Ming Lu
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Abstract

When capturing images through glass surfaces or windshields on rainy days, raindrops and reflections frequently co-occur to significantly reduce the visibility of captured images. This practical problem lacks attention and needs to be resolved urgently. Prior de-raindrop, de-reflection, and all-in-one models have failed to address this composite degradation. To this end, we first formally define the unified removal of raindrops and reflections (UR$^3$) task for the first time and construct a real-shot dataset, namely RainDrop and ReFlection (RDRF), which provides a new benchmark with substantial, high-quality, diverse image pairs. Then, we propose a novel diffusion-based framework (i.e., DiffUR$^3$) with several target designs to address this challenging task. By leveraging the powerful generative prior, DiffUR$^3$ successfully removes both types of degradations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on our benchmark and on challenging in-the-wild images.

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Xingyu Liu, Zewei He, Yu Chen, Chunyu Zhu, Zixuan Chen, Xing Luo, Zhe-Ming Lu. 2026-03-17. Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline. https://arxiv.org/abs/2603.16446

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