菜单

关于 🐙 GitHub
arXiv 提交日期: 2026-06-29
📄 Abstract - Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding

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: this https URL.

顶级标签: computer vision data
详细标签: flicker-banding removal raw image restoration multi-frame fusion dataset benchmark 或 搜索:

从Bricker到BRACE:用于闪烁条纹的包围曝光原始数据集与恢复模型 / Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding


1️⃣ 一句话总结

本文针对相机拍摄屏幕时出现的闪烁条纹问题,通过分析其在不同曝光下的复杂变化,提出了一种基于多帧包围曝光RAW图像的新数据集和恢复模型,能更准确地将条纹与真实纹理区分开并去除,显著提升了图像质量。

源自 arXiv: 2606.29845