SelfHVD：自监督手持视频去模糊
SelfHVD: Self-Supervised Handheld Video Deblurring
提出利用视频中的清晰线索作为自监督信号，并设计自增强视频去模糊与自约束空间一致性维护方法，可复用于其他缺乏配对数据的底层视觉任务。
原文依据 we propose a self-supervised method for handheld video deblurring, which is driven by sharp clues in the video
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论使用手持拍摄设备拍摄视频时，由于手抖和其他不稳定因素，常常导致帧模糊。尽管先前的视频去模糊方法已取得显著进展，但由于训练与测试数据之间的模糊域差距，它们仍难以在真实手持视频上取得令人满意的效果。为解决该问题，我们提出了一种由视频中的清晰线索驱动的自监督手持视频去模糊方法。首先，为了训练去模糊模型，我们从视频中提取清晰线索，并将其作为相邻模糊帧的错位标签。其次，为提高模型的去模糊能力，我们提出了一种新颖的自增强视频去模糊（SEVD）方法来生成更高质量的配对视频数据。第三，我们提出了一种自约束空间一致性维护（SCSCM）方法来正则化模型，防止输出帧与输入帧之间出现位置偏移。此外，我们构建了用于手持视频去模糊的合成和真实世界手持视频数据集。在这些数据集及其他常见真实世界数据集上的大量实验表明，我们的方法显著优于现有的自监督方法。代码和数据集已公开于 https://cshonglei.github.io/SelfHVD。
英文原文摘要 1302 字符
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.