We present RS-Diffusion, the first Diffusion Models-based method for single-frame Rolling Shutter (RS) correction. RS artifacts compromise visual quality of frames due to the row wise exposure of CMOS sensors. Most previous methods have focused on multi-frame approaches, using temporal information from consecutive frames for the motion rectification. However, few approaches address the more challenging but important single frame RS correction. In this work, we present an ``image-to-motion'' framework via diffusion techniques, with a designed patch-attention module. In addition, we present the RS-Real dataset, comprised of captured RS frames alongside their corresponding Global Shutter (GS) ground-truth pairs. The GS frames are corrected from the RS ones, guided by the corresponding Inertial Measurement Unit (IMU) gyroscope data acquired during capture. Experiments show that our RS-Diffusion surpasses previous single RS correction methods. Our method and proposed RS-Real dataset lay a solid foundation for advancing the field of RS correction.

翻译：我们提出了RS-Diffusion，这是首个基于扩散模型（Diffusion Models）的单帧卷帘快门（Rolling Shutter, RS）校正方法。由于CMOS传感器的逐行曝光特性，RS伪影会损害图像的视觉质量。以往的研究大多集中于多帧方法，利用连续帧之间的时序信息进行运动校正。然而，针对更具挑战性但十分重要的单帧RS校正问题，现有方法却很少涉及。在本工作中，我们通过扩散技术提出了一个“图像到运动”的框架，并设计了一个块注意力模块。此外，我们提出了RS-Real数据集，该数据集包含采集的RS帧及其对应的全局快门（Global Shutter, GS）真值对。GS帧是在采集过程中获取的惯性测量单元（IMU）陀螺仪数据的引导下，从RS帧校正得到的。实验表明，我们的RS-Diffusion方法超越了以往的单帧RS校正方法。我们的方法和提出的RS-Real数据集为推进RS校正领域的研究奠定了坚实的基础。