  The vast majority of modern consumer-grade cameras employ a rolling shutter
mechanism, leading to image distortions if the camera moves during image
acquisition. In this paper, we present a novel deep network to solve the
generic rolling shutter correction problem with two consecutive frames. Our
pipeline is symmetrically designed to predict the global shutter image
corresponding to the intermediate time of these two frames, which is difficult
for existing methods because it corresponds to a camera pose that differs most
from the two frames. First, two time-symmetric dense undistortion flows are
estimated by using well-established principles: pyramidal construction,
warping, and cost volume processing. Then, both rolling shutter images are
warped into a common global shutter one in the feature space, respectively.
Finally, a symmetric consistency constraint is constructed in the image decoder
to effectively aggregate the contextual cues of two rolling shutter images,
thereby recovering the high-quality global shutter image. Extensive experiments
with both synthetic and real data from public benchmarks demonstrate the
superiority of our proposed approach over the state-of-the-art methods.
