  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.
