解决屏幕拍摄时的闪烁条纹问题，提出新数据集与多帧修复模型。
Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding
- 用物理模拟生成多曝光原始图像数据，构建新数据集Bricker。
- 提出BRACE模型，通过频域先验和跨曝光融合，有效去除闪烁条纹。
- 适用于视频拍摄、屏幕内容处理等需要高保真还原的场景。
闪烁条纹（Flicker-banding, FB）由相机滚动快门与显示器亮度调制之间的时序混叠引起，导致屏幕捕获图像出现色偏和锯齿图案，影响可读性。现有单帧方法依赖简化参数化条纹模型，难以区分真实纹理与伪影。本文系统分析复杂FB形态，发现其在不同曝光设置下存在显著差异，由此提出多帧分段原始图像恢复范式。构建了基于光线追踪物理模拟与自动化多曝光采集工具的合成-真实混合数据集Bricker。进一步提出BRACE模型：利用频率感知的条纹先验和多尺度空间交叉注意力调制器（MSCAM），实现跨曝光空间融合。引入条纹频率一致性（SFC）指标评估去条纹效果。实验在合成与真实基准上均达到先进水平。代码与数据集已开源。
原文摘要 · Abstract (English)
Flicker-banding (FB), arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color shifts and jagged patterns. Existing single-frame methods with simplified parametric stripe models cannot reliably distinguish these artifacts from genuine texture. To address this, we conduct a systematic analysis of complex FB morphologies and reveal their significant variation across exposure settings, motivating a multi-frame bracketed RAW restoration paradigm. We construct Bricker, a synthetic-real bracketed RAW dataset built via ray-tracing-based physical simulation and automated multi-exposure capture tool. We further propose BRACE: Bracketed RAW Flicker-Banding Removal, a multi-frame restoration model that utilizes frequency-aware banding prior and a multi-scale spatial cross-attention modulator (MSCAM) for cross-exposure spatial fusion. We also introduce the Stripe Frequency Consistency (SFC) metric to evaluate banding removal. Experiments demonstrate state-of-the-art performance on both synthetic and real benchmarks. Our dataset and code are available at: https://github.com/ZZH-qwq/BRACE.
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