Abstract
Lens flare is a degradation phenomenon caused by strong light sources. Existing researches on flare removal have mainly focused on images, while the spatiotemporal characteristics of video flare remain largely unexplored. Video flare synthesis and removal pose significantly greater challenges than in image, owing to the complex and mutually independent motion of flare, light sources, and scene content. This motion independence further affects restoration performance, often resulting in flicker and artifacts. To address this issue, we propose a physics-informed dynamic flare synthesis pipeline, which simulates light source motion using optical flow and models the temporal behaviors of both scattering and reflective flares. Meanwhile, we design a video flare removal network that employs an attention module to spatially suppress flare regions and incorporates a Mamba-based temporal modeling component to capture long range spatio-temporal dependencies. This motion-independent spatiotemporal representation effectively eliminates the need for multi-frame alignment, alleviating temporal aliasing between flares and scene content and thereby improving video flare removal performance. Building upon this, we construct the first video flare dataset to comprehensively evaluate our method, which includes a large set of synthetic paired videos and additional real-world videos collected from the Internet to assess generalization capability. Extensive experiments demonstrate that our method consistently outperforms existing video-based restoration and image-based flare removal methods on both real and synthetic videos, effectively removing dynamic flares while preserving light source integrity and maintaining spatiotemporal consistency of scene.
Abstract (translated)
镜头光晕是一种由强光源引起的退化现象。现有的关于去除光晕的研究主要集中在图像上，而视频中的光晕的空间和时间特性则鲜有研究。与图像相比，视频光晕的合成和去除面临更大的挑战，原因在于光晕、光源以及场景内容之间的复杂且相互独立的运动模式进一步影响了修复效果，通常会导致闪烁和伪影。为了解决这个问题，我们提出了一种基于物理信息的动态光晕合成管道，该管道利用光学流模拟光源运动，并对散射和反射性光晕的时间行为进行建模。与此同时，我们设计了一个视频光晕去除网络，其中运用了注意力模块来抑制空间上的光晕区域，并整合了一个基于Mamba的时间模型组件以捕捉长程时空依赖关系。这种独立于运动的时空表示有效地消除了多帧对齐的需求，减轻了光晕与场景内容之间的时间混叠问题，从而提升了视频光晕去除的效果。 在此基础上，我们构建了首个用于全面评估我们方法的视频光晕数据集，其中包括大量的合成配对视频以及从互联网收集的真实世界视频以测试泛化能力。广泛的实验表明，我们的方法在真实和合成视频上始终优于现有的基于视频恢复和图像去光晕的方法，能够有效地去除动态光晕的同时保持光源的完整性，并维持场景时空一致性。
URL
https://arxiv.org/abs/2512.11327
