# EvReflection: Event-Driven Micro-Dynamics for Reflection Removal

arXiv: https://arxiv.org/abs/2608.06184 · HTML: https://mlsanity.com/paper/2608.06184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [Focal Modulation Networks](https://mlsanity.com/paper/2203.11926/) (2022)
- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [Single Image Reflection Separation with Perceptual Losses](https://mlsanity.com/paper/1806.05376/) (2018)
- [Single Image Reflection Removal through Cascaded Refinement](https://mlsanity.com/paper/1911.06634/) (2019)
- [EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://mlsanity.com/paper/1912.01584/) (2019)
- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [Location-aware Single Image Reflection Removal](https://mlsanity.com/paper/2012.07131/) (2020)
- [Single Image Reflection Separation via Component Synergy](https://mlsanity.com/paper/2308.10027/) (2023)
- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2503.17673/) (2025)
- [Revisiting Single Image Reflection Removal In the Wild](https://mlsanity.com/paper/2311.17320/) (2023)
- [Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation](https://mlsanity.com/paper/2110.10546/) (2021)
- [Reversible Decoupling Network for Single Image Reflection Removal](https://mlsanity.com/paper/2410.08063/) (2024)
- [Learning to See Through Obstructions with Layered Decomposition](https://mlsanity.com/paper/2008.04902/) (2020)
- [Learned Dual-View Reflection Removal](https://mlsanity.com/paper/2010.00702/) (2020)
- [Dereflection Any Image with Diffusion Priors and Diversified Data](https://mlsanity.com/paper/2503.17347/) (2025)
- [EvTexture: Event-driven Texture Enhancement for Video Super-Resolution](https://mlsanity.com/paper/2406.13457/) (2024)
- [Hybrid Spiking Vision Transformer for Object Detection with Event Cameras](https://mlsanity.com/paper/2505.07715/) (2025)
- [Event-Enhanced Blurry Video Super-Resolution](https://mlsanity.com/paper/2504.13042/) (2025)
- [DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation](https://mlsanity.com/paper/2303.08977/) (2023)
- [Seeing the Unseen: Zooming in the Dark with Event Cameras](https://mlsanity.com/paper/2601.02206/) (2026)

## Built on by (most cited first, 1 of 1)

- [E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding](https://mlsanity.com/paper/2609.34346/) (2026)

## Abstract (arXiv)

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 EVR2 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.