  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.
