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

Bringing Rolling Shutter Images Alive with Dual Reversed Distortion