结合闪烁条纹和摩尔纹去除的屏幕捕获图像处理
Combined Flicker-banding and Moire Removal for Screen-Captured Images
AI总结：
本文提出CLEAR框架，通过联合去除屏幕捕获图像中的摩尔纹和闪烁条纹，提升图像质量。
摘要：
使用移动设备捕捉显示屏已成为一种常见做法，但所获得的图像往往因摩尔纹和闪烁条纹的共存而遭受严重退化，导致显著的视觉质量下降。由于这两种伪影在真实成像过程中具有强耦合性，现有针对单一退化设计的方法无法推广到此类复合场景。本文首次系统研究了屏幕捕获图像中摩尔纹和闪烁条纹的联合去除，并提出了一种统一的修复框架，称为CLEAR。为了支持此任务，我们构建了一个包含摩尔纹和闪烁条纹的大型数据集，并引入了一个基于图像信号处理（ISP）的闪烁模拟管道，以稳定模型训练并扩展退化分布。此外，我们设计了一个频域分解与重构模块以及一个轨迹对齐损失，以增强对复合伪影的建模。大量实验表明，所提出的方法在多种评估指标上均优于现有图像修复方法，验证了其在复杂真实场景中的有效性。
英文摘要：
Capturing display screens with mobile devices has become increasingly common, yet the resulting images often suffer from severe degradations caused by the coexistence of moiré patterns and flicker-banding, leading to significant visual quality degradation. Due to the strong coupling of these two artifacts in real imaging processes, existing methods designed for single degradations fail to generalize to such compound scenarios. In this paper, we present the first systematic study on joint removal of moiré patterns and flicker-banding in screen-captured images, and propose a unified restoration framework, named CLEAR. To support this task, we construct a large-scale dataset containing both moiré patterns and flicker-banding, and introduce an ISP-based flicker simulation pipeline to stabilize model training and expand the degradation distribution. Furthermore, we design a frequency-domain decomposition and re-composition module together with a trajectory alignment loss to enhance the modeling of compound artifacts. Extensive experiments demonstrate that the proposed method consistently. outperforms existing image restoration approaches across multiple evaluation metrics, validating its effectiveness in complex real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
