EvReflection：用于去除反射的事件驱动微动力学
EvReflection: Event-Driven Micro-Dynamics for Reflection Removal
AI总结：
本文提出事件驱动反射去除网络EvReflection，利用事件信号打破反射层与透射层的歧义，结合微动力学解耦器与视差注意力矫正器，在合成和真实基准上PSNR分别提升超1.6 dB、1.2 dB，构建了首个该任务真实数据集EVR²。
AI中文摘要：
尽管反射去除领域已取得显著进展，但现有方法主要利用单帧静态图像先验，且因反射层与透射层之间固有的歧义性，仍存在严重的残留伪影。本文提出利用事件信号打破这种歧义性：通过事件相机捕捉微动力学，揭示两层之间的差分运动，进而提出一种新颖的事件驱动反射去除网络EvReflection，利用这些动态线索进行层分离。具体而言，我们设计了微动力学解耦器，从事件流中解耦出层特定运动作为先验，再引导视差注意力矫正器从RGB图像中干净地去除伪影；此外，为解决数据稀缺问题，我们开发了视差感知模拟流程，并构建了首个面向该任务的真实世界数据集EVR²基准。大量实验表明，EvReflection在合成与真实世界基准上均达到了当前最优性能，分别在PSNR上较最佳对比方法提升超过1.6 dB和1.2 dB。代码、数据集及预训练模型可在该https URL获取。
英文摘要：
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.
