Bringing Rolling Shutter Images Alive with Dual Reversed Distortion

TL;DR

This paper introduces IFED, an end-to-end model that leverages dual reversed rolling shutter images to effectively reverse RS distortion and recover global shutter frames, outperforming existing methods even on real-world dynamic scenes.

Contribution

The paper proposes a novel dual RS camera setup and an end-to-end learning model, IFED, to improve the reversal of RS distortion beyond prior geometric correlation approaches.

Findings

IFED outperforms naive cascade schemes.

Effective on real-world dynamic scenes.

Trained on synthetic data, generalizes well.

Abstract

Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera. This means that the information of each instant GS frame is partially, yet sequentially, embedded into the row-dependent distortion. Inspired by this fact, we address the challenging task of reversing this process, i.e., extracting undistorted GS frames from images suffering from RS distortion. However, since RS distortion is coupled with other factors such as readout settings and the relative velocity of scene elements to the camera, models that only exploit the geometric correlation between temporally adjacent images suffer from poor generality in processing data with different readout settings and dynamic scenes with both camera motion and object motion. In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task. Grounded on the symmetric and complementary nature of dual reversed distortion, we develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time. Extensive experimental results demonstrate that IFED is superior to naive cascade schemes, as well as the state-of-the-art which utilizes adjacent RS images. Most importantly, although it is trained on a synthetic dataset, IFED is shown to be effective at retrieving GS frame sequences from real-world RS distorted images of dynamic scenes. Code is available at https://github.com/zzh-tech/Dual-Reversed-RS.