RIFLE：通过潜在扩散增强去除图像闪烁条纹
RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement
AI总结：
本文提出RIFLE，一种基于扩散模型的框架，用于去除屏幕拍摄图像中的闪烁条纹，通过先验估计器和掩码损失保留细节，并构建模拟流程和真实数据集，效果优于现有基线。
摘要：
拍摄屏幕已成为我们日常生活中的常规操作。然而，发射型显示器的照片常常受到闪烁条纹（FB）的影响，这种条纹是由相机卷帘快门读出与显示器亮度调制之间的时间混叠产生的明暗交替条纹。与已被广泛研究的摩尔纹退化不同，FB尽管对可读性和感知质量有频繁且严重的影响，却仍未得到充分探索。我们将FB去除定义为一个专门的修复任务，并提出了通过潜在扩散增强去除图像闪烁条纹（RIFLE），这是一个基于扩散的框架，旨在去除FB同时保留精细细节。我们提出了闪烁条纹先验估计器（FPE），用于预测关键条纹属性并将其注入修复网络。此外，我们提出了掩码损失（ML），以在不牺牲全局保真度的情况下将监督集中在条纹区域。为克服数据稀缺问题，我们提供了一个模拟流程，在亮度域中合成FB，并在条纹角度、条纹间距和条纹宽度上加入随机抖动。同时应用羽化边界和传感器噪声以实现更逼真的模拟。为进行评估，我们收集了一个成对的真实世界FB数据集，其中包含通过长时间曝光捕获的像素对齐的无条纹参考。在我们的真实世界数据集上的定量指标和视觉比较中，RIFLE在从轻微到严重的闪烁条纹情况下始终优于最近的图像重建基线。据我们所知，这是首个研究FB模拟和去除的工作。我们的工作为后续在数据集构建和去除模型设计方面的研究奠定了坚实基础。我们的数据集和代码将很快发布。
英文摘要：
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.
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