用事件相机捕捉微动态，解决反射去除中的残余伪影问题。
EvReflection: Event-Driven Micro-Dynamics for Reflection Removal
- 利用事件信号揭示反射与透射层的运动差异，作为分离先验。
- 在合成和真实数据集上分别提升1.6dB和1.2dB的PSNR性能。
- 首个面向该任务的真实世界数据集EVRR²，适合视觉去反射研究者。
尽管反射去除取得显著进展，现有方法主要依赖单帧静态图像先验，仍因反射与透射层固有的模糊性导致严重残余伪影。本文提出利用事件信号打破此模糊性，通过事件相机捕捉微动态，揭示两层间的运动差异。为此，我们设计了事件驱动的反射去除网络EvReflection，利用动态线索实现层分离。具体地，提出微动态解耦模块从事件流中解耦出层特定运动作为先验，并由视差注意力修正器引导干净地去除RGB图像中的伪影。此外，为应对数据稀缺问题，构建了一个视差感知仿真管道，建立了首个真实世界的基准数据集EVR²。大量实验表明，EvReflection在合成与真实世界基准上均达到领先性能，相较最优方法分别提升超过1.6 dB和1.2 dB的PSNR。代码、数据集及预训练模型已公开于https://github.com/JiaxiaoWang/EvReflection。
原文摘要 · Abstract (English)
Despite remarkable progress in reflection removal, current methods primarily exploit static image priors from a single frame and still suffer from severe residual artifacts due to the inherent ambiguity between the reflection and transmission layers. In this paper, we propose leveraging event signals to break this ambiguity. By employing event cameras to capture micro-dynamics, we reveal the differential motion between these two layers. We thereby present a novel event-driven reflection removal network, EvReflection, that utilizes these dynamic cues for layer separation. Specifically, we design a Micro-Dynamics Decoupler to disentangle layer-specific motions from event streams as priors, which then guide a Parallax-Attention Rectifier to cleanly remove artifacts from the RGB image. Furthermore, to address data scarcity, we develop a parallax-aware simulation pipeline and construct the EVR$^2$ benchmark dataset, the first real-world dataset for this task. Extensive experiments demonstrate that EvReflection achieves state-of-the-art performance on both synthetic and real-world benchmarks, surpassing the best competing method by more than 1.6 dB and 1.2 dB in PSNR, respectively. The code, dataset, and pre-trained models are available at https://github.com/JiaxiaoWang/EvReflection.
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