用事件相机提升雾霾图像去模糊效果，首次实现高动态范围信息引导的清晰成像。
From Events to Clarity: The Event-Guided Diffusion Framework for Dehazing
- 引入事件相机的高动态范围特性，通过扩散模型融合事件与视觉信息。
- 在真实重霾场景（AQI=341）下实现最佳去雾效果，结构细节更清晰。
- 适合需要高精度、高动态场景感知的自动驾驶与无人机应用。
雾霾条件下的清晰成像是关键任务。传统基于先验和神经网络的方法依赖于RGB图像，但受限于动态范围（60 dB），难以保留结构与光照细节。为此，本文首次将事件相机用于去雾，其具备120 dB的超大动态范围和微秒级延迟，更适合雾霾环境。由于真实配对数据稀缺，事件信息向图像迁移困难。我们提出一种事件引导的扩散模型，利用扩散模型的强大生成先验，从有雾输入中重建清晰图像，并有效传递事件中的高动态范围信息。设计事件引导模块，将稀疏的高动态特征（如边缘、角点）映射到扩散潜空间，提供精确结构指导，提升视觉真实感并减少语义漂移。为验证实用性，我们采集了同步配备RGB与事件传感器的无人机数据集，覆盖重度雾霾（AQI=341）。在两个基准数据集及自建数据集上均取得当前最优性能。
原文摘要 · Abstract (English)
Clear imaging under hazy conditions is a critical task. Prior-based and neural methods have improved results. However, they operate on RGB frames, which suffer from limited dynamic range. Therefore, dehazing remains ill-posed and can erase structure and illumination details. To address this, we use event cameras for dehazing for the \textbf{first time}. Event cameras offer much higher HDR ($120 dBvs.60 dB$) and microsecond latency, therefore they suit hazy scenes. In practice, transferring HDR cues from events to frames is hard because real paired data are scarce. To tackle this, we propose an event-guided diffusion model that utilizes the strong generative priors of diffusion models to reconstruct clear images from hazy inputs by effectively transferring HDR information from events. Specifically, we design an event-guided module that maps sparse HDR event features, \textit{e.g.,} edges, corners, into the diffusion latent space. This clear conditioning provides precise structural guidance during generation, improves visual realism, and reduces semantic drift. For real-world evaluation, we collect a drone dataset in heavy haze (AQI = 341) with synchronized RGB and event sensors. Experiments on two benchmarks and our dataset achieve state-of-the-art results.
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