自监督方法提升手持视频去模糊效果，解决真实场景与训练数据的差异问题。
SelfHVD: Self-Supervised Handheld Video Deblurring
- 利用视频中固有的清晰线索作为伪标签，实现无需真实清晰帧的自监督训练。
- 通过自增强生成更高质量成对视频数据，提升模型去模糊能力。
- 提出空间一致性约束机制，防止输出帧与输入帧出现位置偏移，适合实际手持拍摄场景。
手持设备拍摄视频常因手抖等不稳定性导致画面模糊。尽管已有去模糊方法取得进展，但在真实手持视频上仍受训练与测试数据间模糊域差距的限制。为此，我们提出自监督手持视频去模糊方法SelfHVD，其核心思路是利用视频中的清晰线索作为相邻模糊帧的错位标签进行训练。为提升模型性能，提出自增强视频去模糊（SEVD）方法，生成更高品质的成对视频数据；同时设计自约束空间一致性保持（SCSCM）方法，防止输出与输入帧间发生位置偏移。此外，构建了合成与真实手持视频数据集。在多个常见真实数据集上的实验表明，本方法显著优于现有自监督方法。代码与数据集已公开于https://cshonglei.github.io/SelfHVD。
原文摘要 · Abstract (English)
Shooting video with handheld shooting devices often results in blurry frames due to shaking hands and other instability factors. Although previous video deblurring methods have achieved impressive progress, they still struggle to perform satisfactorily on real-world handheld video due to the blur domain gap between training and testing data. To address the issue, we propose a self-supervised method for handheld video deblurring, which is driven by sharp clues in the video. First, to train the deblurring model, we extract the sharp clues from the video and take them as misalignment labels of neighboring blurry frames. Second, to improve the deblurring ability of the model, we propose a novel Self-Enhanced Video Deblurring (SEVD) method to create higher-quality paired video data. Third, we propose a Self-Constrained Spatial Consistency Maintenance (SCSCM) method to regularize the model, preventing position shifts between the output and input frames. Moreover, we construct synthetic and real-world handheld video datasets for handheld video deblurring. Extensive experiments on these and other common real-world datasets demonstrate that our method significantly outperforms existing self-supervised ones. The code and datasets are publicly available at https://cshonglei.github.io/SelfHVD.
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