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.

翻译：现代消费级相机通常采用卷帘快门（RS）机制，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）图像作为真实标注监督。本文提出针对双反向RS畸变校正的自监督学习框架（SelfDRSC），仅基于具有反向畸变的双RS图像即可学习DRSC网络生成高帧率GS视频。具体而言，我们提出双向畸变扭曲模块用于重建双反向RS图像，进而部署自监督损失函数，通过增强输入与重建双反向RS图像间的循环一致性来训练DRSC网络。除起始和结束RS扫描时刻外，SelfDRSC还能对任意中间扫描时刻的GS图像进行监督，从而使训练后的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简单有效的自蒸馏策略，以减弱生成GS图像中的边界伪影。在合成数据集上，SelfDRSC相比全监督训练的先进方法取得更优或相当的评价指标。针对真实RS场景，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。源代码及预训练模型已在https://github.com/shangwei5/SelfDRSC 公开，同时在华为昇思MindSpore框架下的实现见https://github.com/Hunter-Will/SelfDRSC-mindspore。