用扩散模型去除屏幕拍照的闪烁条纹，保留细节并提升画质。
RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement
- 基于扩散模型设计修复框架，引入闪烁先验估计器增强恢复能力。
- 在真实数据集上，对轻度到重度闪烁条纹均优于现有方法。
- 首创仿真生成与真实配对数据集，推动该领域研究发展。
如今拍摄显示屏已成为日常，但相机滚动快门与屏幕亮度调制之间的时序混叠常导致闪烁条纹（FB）——明暗交替的条带，严重影响可读性与观感质量。不同于已被广泛研究的摩尔纹，FB仍缺乏系统探索。本文将FB去除视为专门的图像修复任务，提出基于扩散模型的RIFLE框架，通过闪烁先验估计器（FPE）预测条纹关键属性并注入恢复网络，并设计掩码损失（ML）聚焦于条纹区域以保持整体一致性。针对数据稀缺问题，构建了在亮度域中模拟闪烁条纹的合成流程，加入条纹角度、间距、宽度的随机抖动，以及羽化边界和传感器噪声以提升真实性。为评估，收集了像素级对齐的真实世界带条纹与无条纹参考图像数据集。在该数据集上，无论定量指标还是视觉对比，RIFLE均持续优于近期图像重建基线方法。据我们所知，这是首个研究闪烁条纹模拟与去除的工作，为后续数据构建与模型设计奠定基础。数据集与代码即将发布。
原文摘要 · Abstract (English)
Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB), which is alternating bright%u2013dark stripes that arise from temporal aliasing between a camera's rolling-shutter readout and the display's brightness modulation. Unlike moire degradation, which has been extensively studied, the FB remains underexplored despite its frequent and severe impact on readability and perceived quality. We formulate FB removal as a dedicated restoration task and introduce Removal of Image Flicker-Banding via Latent Diffusion Enhancement, RIFLE, a diffusion-based framework designed to remove FB while preserving fine details. We propose the flicker-banding prior estimator (FPE) that predicts key banding attributes and injects it into the restoration network. Additionally, Masked Loss (ML) is proposed to concentrate supervision on banded regions without sacrificing global fidelity. To overcome data scarcity, we provide a simulation pipeline that synthesizes FB in the luminance domain with stochastic jitter in banding angle, banding spacing, and banding width. Feathered boundaries and sensor noise are also applied for a more realistic simulation. For evaluation, we collect a paired real-world FB dataset with pixel-aligned banding-free references captured via long exposure. Across quantitative metrics and visual comparisons on our real-world dataset, RIFLE consistently outperforms recent image reconstruction baselines from mild to severe flicker-banding. To the best of our knowledge, it is the first work to research the simulation and removal of FB. Our work establishes a great foundation for subsequent research in both the dataset construction and the removal model design. Our dataset and code will be released soon.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。