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.

翻译：现代消费相机通常采用卷帘快门（RS）机制，其通过逐行扫描场景捕获图像，导致动态场景产生RS畸变。为校正RS畸变，现有方法采用全监督学习方式，需采集高帧率全局快门（GS）图像作为真值监督。本文提出一种针对双反向RS畸变校正的自监督学习框架（SelfDRSC），其仅基于具有反向畸变的双RS图像即可学习生成高帧率GS视频。具体而言，我们设计了一种双向畸变扭曲模块用于重建双反向RS图像，随后通过增强输入与重建双反向RS图像之间的循环一致性，部署自监督损失以训练DRSC网络。除起始和结束RS扫描时刻外，SelfDRSC还可对任意中间扫描时刻的GS图像进行监督，从而使得训练的DRSC网络能够生成高帧率GS视频。此外，我们在自监督损失中引入一种简单有效的自蒸馏策略，以抑制生成GS图像中的边界伪影。在合成数据集上，SelfDRSC相较于全监督方式训练的最先进方法取得了更优或可比的量化指标。在真实RS场景中，我们的SelfDRSC能够生成具有更精细校正纹理和更好时间一致性的高帧率GS视频。源代码与训练模型已在https://github.com/shangwei5/SelfDRSC 公开。