  Modern consumer cameras usually employ the rolling shutter (RS) mechanism,
where images are captured by scanning scenes row-by-row, yielding RS
distortions for dynamic scenes. To correct RS distortions, existing methods
adopt a fully supervised learning manner, where high framerate global shutter
(GS) images should be collected as ground-truth supervision. In this paper, we
propose a Self-supervised learning framework for Dual reversed RS distortions
Correction (SelfDRSC), where a DRSC network can be learned to generate a high
framerate GS video only based on dual RS images with reversed distortions. In
particular, a bidirectional distortion warping module is proposed for
reconstructing dual reversed RS images, and then a self-supervised loss can be
deployed to train DRSC network by enhancing the cycle consistency between input
and reconstructed dual reversed RS images. Besides start and end RS scanning
time, GS images at arbitrary intermediate scanning time can also be supervised
in SelfDRSC, thus enabling the learned DRSC network to generate a high
framerate GS video. Moreover, a simple yet effective self-distillation strategy
is introduced in self-supervised loss for mitigating boundary artifacts in
generated GS images. On synthetic dataset, SelfDRSC achieves better or
comparable quantitative metrics in comparison to state-of-the-art methods
trained in the full supervision manner. On real-world RS cases, our SelfDRSC
can produce high framerate GS videos with finer correction textures and better
temporary consistency. The source code and trained models are made publicly
available at https://github.com/shangwei5/SelfDRSC. We also provide an
implementation in HUAWEI Mindspore at
https://github.com/Hunter-Will/SelfDRSC-mindspore.
