发表机构
Graduate School of Information Science and Technology, the University of Tokyo（东京大学信息科学与技术研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CMOS卷帘快门及其全局复位模式在动态范围与速度上的缺陷，提出双反向扫描与行自适应对齐及混合注意力网络，实现高动态范围照片序列拍摄，并构建真实数据集验证效果。
AI 中文摘要
CMOS传感器在移动摄影中的采用经常受到卷帘快门（RS）效应的损害，该效应引入几何畸变和运动伪影。特别是，最近的具有全局复位的卷帘快门（RSGR）模式，在缓解某些RS问题的同时，也带来了重大限制，包括降低的拍摄速度和压缩的动态范围。为了解决这些问题，我们提出了一种新颖的双反向扫描设置，同时利用RSGR和反向RSGR视图。该解决方案不仅通过同步互补曝光以平衡帧间的动态范围来处理RSGR的固有缺陷，而且引入了一种在高动态场景下进行HDR照片序列拍摄的有效方法。我们提出的网络首先适应行间互补性，并通过行自适应特征对齐来处理视觉偏移。随后，基于相关性引导的混合注意力块构建的幻觉模块，整合相互增强的特征以恢复缺失的细节。此外，我们构建了一个同轴成像系统来收集真实世界数据集，使得在数值模拟之外能够进行稳健的训练和评估。实验结果表明，我们的解决方案在缓解RSGR限制和推进HDR重建技术方面具有双重益处。
英文摘要
The adoption of CMOS sensors in mobile photography is frequently compromised by the rolling shutter (RS) effect, which introduces geometric distortions and motion artifacts. Particularly, recent rolling shutter with global reset (RSGR) mode, while mitigating some RS issues, also incurs major limitations, including reduced capture speed and compressed dynamic range. To address these problems, we propose a novel dual reversed scanning setup utilizing both RSGR and inverted RSGR views. This solution not only handles the inherent flaws of RSGR by synchronizing complementary exposures to balance the dynamic range across the frames but also introduces an effective method for HDR photosequencing under highly dynamic scenes. Our proposed network first accommodates row-wise complementarity and manages visual shifts by row-adaptive feature alignment. Subsequently, the hallucination module, built upon a correlation-guided mixattention block, integrates the mutually reinforced features to recover missing details. In addition, we construct a coaxial imaging system to collect a real-world dataset, enabling robust training and evaluation beyond numerical simulation. Experimental results demonstrate the twofold benefits of our solution in mitigating RSGR limitations and advancing HDR reconstruction techniques.
