Rolling shutter CMOS cameras are widely used in mobile and embedded vision, but rapid motion and vibration often cause coupled degradations, including motion blur and rolling shutter (RS) geometric distortion. This paper presents a visual–inertial fusion framework that estimates unified motion-related degradation parameters from IMU and image measurements and uses them to restore both photometric and geometric image quality in high-dynamic scenes. We further introduce an exposure-aware deblurring pipeline that accounts for the nonlinear photoelectric conversion characteristics of CMOS sensors, as well as a perspective-consistent RS compensation method to improve geometric consistency under depth–motion coupling. Experiments on real mobile data and public RS-visual–inertial sequences demonstrate improved image quality and downstream SLAM pose accuracy compared with representative baselines.
Ye et al. (Thu,) studied this question.