# Visual–Inertial Fusion-Based Restoration of Image Degradation in High-Dynamic Scenes with Rolling Shutter Cameras

## TL;DR

This paper introduces a method to restore image quality degraded by motion and rolling shutter effects using visual-inertial fusion in high-dynamic scenes.

## Contribution

A novel visual-inertial fusion framework that jointly restores photometric and geometric image quality in rolling shutter cameras.

## Key findings

- The proposed framework improves image quality in high-dynamic scenes with rolling shutter distortion.
- Experiments show better SLAM pose accuracy compared to existing methods.
- An exposure-aware deblurring pipeline and perspective-consistent compensation method are introduced.

## Abstract

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.