This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firstly, they face challenges in estimating the accurate correction field due to the uniform velocity assumption, leading to significant image correction errors under complex motion. Secondly, the drastic occlusion in dynamic scenes prevents current solutions from achieving better image quality because of the inherent difficulties in aligning and aggregating multiple frames. To tackle these challenges, we model the curvilinear trajectory of pixels analytically and propose a geometry-based Quadratic Rolling Shutter (QRS) motion solver, which precisely estimates the high-order correction field of individual pixel. Besides, to reconstruct high-quality occlusion frames in dynamic scenes, we present a 3D video architecture that effectively Aligns and Aggregates multi-frame context, namely, RSA^2-Net. We evaluate our method across a broad range of cameras and video sequences, demonstrating its significant superiority. Specifically, our method surpasses the state-of-the-arts by +4.98, +0.77, and +4.33 of PSNR on Carla-RS, Fastec-RS, and BS-RSC datasets, respectively.

翻译：本文针对复杂非线性及动态场景中极端遮挡下的卷帘快门校正问题展开研究。现有方法存在两大缺陷：首先，基于匀速运动假设难以准确估计校正场，导致复杂运动场景下图像校正误差显著；其次，动态场景中的严重遮挡使得现有方案因多帧对齐与聚合的固有困难而无法获得更优的图像质量。为应对这些挑战，我们通过解析建模像素的曲线轨迹，提出一种基于几何的二次卷帘快门（QRS）运动求解器，可精确估计每个像素的高阶校正场。此外，为重建动态场景中的高质量遮挡帧，我们提出一种有效对齐与聚合多帧上下文的3D视频架构——RSA^2-Net。通过涵盖多种相机与视频序列的广泛评估，本方法展现出显著优势。具体而言，在Carla-RS、Fastec-RS与BS-RSC数据集上，本方法PSNR指标分别超越现有最优方法4.98、0.77与4.33 dB。