发表机构
Khulna University of Engineering & Technology（库尔纳工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文重新审视高速视频合成运动去模糊数据集的标签有效性，提出精确条件（奇数窗口与等时长），并审计发现消费级设备时序偏差，且奇偶选择影响大于时序不规则，提供检查工具并发布数据。
AI 中文摘要
运动去模糊数据集通常通过平均N个连续的高帧率帧并以中心帧作为标签来合成。我们证明，仅当窗口为奇数且帧的采样持续时间相等时，该标签对于每次采集时序才是无偏的。偶数窗口会使每个标签偏移模糊长度的一个固定比例，即使在完美时序下也是如此。不等的持续时间则更为微妙：它们的平均错位为零，因此数据集统计无法揭示它们，然而当偏移无法从模糊中读取时，它们会卷积监督信号而非向其添加噪声。审计51个以标称240 fps录制的智能手机片段，我们发现19个实际以约176 fps采集，这一行为仅记录在容器的时序表中。在受控测试中，奇偶选择给拟合的线性去模糊滤波器带来的代价远大于这些时序不规则性，而插值标签（不同于模糊标签）可以用真实帧时间进行修复。我们提供了一个无需解码即可检查这两个条件的工具，并将发布这些片段及其时序表。
英文摘要
Motion-deblurring datasets are commonly synthesised by averaging $N$ consecutive high-frame-rate frames and labelling the result with the central frame. We show that this label is unbiased for every capture timing only when the window is odd and the frames' sample durations are equal. An even window shifts every label by a fixed fraction of the blur length, even under perfect timing. Unequal durations are subtler: their mean misalignment is zero, so dataset statistics cannot reveal them, yet when the offset cannot be read from the blur they convolve the supervision rather than adding noise to it. Auditing 51 smartphone clips recorded at a nominal 240 fps, we find 19 captured near 176 fps, a behaviour recorded only in the container's timing tables. In a controlled test the parity choice costs a fitted linear deblurring filter far more than these timing irregularities do, and interpolation labels, unlike blur labels, can be repaired with the true frame times. We provide a tool that checks both conditions without decoding, and will release the clips and their timing tables.
