面向模糊到视频分解的时间特化事件-图像对齐
Time-Specialized Event-Image Alignment for Blur-to-Video Decomposition
把事件相机的微秒级时序信息用相对时间编码注意力与时间面动态形变显式对齐到任意查询时刻，为从单张模糊图恢复高帧率视频提供了可复用的时序对齐范式，也可迁移到事件引导的插帧与去模糊任务。
原文依据 we introduce a Relative Time-Encoded Attention module that steers event features toward motion information relevant to a given target time
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论运动模糊是动态成像中常见的退化现象。近期研究已不再局限于从模糊输入中恢复单张清晰图像，而是转向模糊分解：从一张运动模糊图像中恢复时间上连续的清晰视频序列。事件相机具有微秒级时间分辨率，可有效缓解运动歧义。然而，现有基于事件的方法往往未能显式建模时间对齐的事件-图像特征。如何准确利用事件数据重建不同时刻的帧仍 largely 未被充分探索。本文提出 TSANet，一种基于事件的模糊到视频分解方法，对事件特征与图像特征同时进行时间特化以实现对齐。具体而言，我们引入相对时间编码注意力模块，将事件特征引导至与给定目标时间相关的运动信息；并引入时间面动态形变模块，将图像特征形变到与该时刻对应的空间构型。借助在任意查询时刻显式对齐的时间特化运动特征与图像特征，我们的框架可将单张模糊图像分解为高帧率清晰视频序列。此外，我们采集了一个包含真实事件与高质量彩色视频的新数据集，并通过平均清晰帧合成模糊输入以评测我们的方法。在多个数据集上使用合成事件与真实事件的实验表明，我们的方法在模糊分解任务上持续优于此前的最优方法。
英文原文摘要 1581 字符
Motion blur is a common degradation in dynamic imaging. Recent studies have moved beyond restoring a single sharp image from a blurred input and instead target blur decomposition: recovering a temporally continuous sharp video sequence from one motion-blurred image. Event cameras, with their microsecond temporal resolution, can effectively alleviate motion ambiguity. However, existing event-based methods often fail to explicitly model time-aligned event-image features. How to accurately exploit event data to reconstruct frames at different time instants remains largely underexplored. In this paper, we propose TSANet, an event-based blur-to-video decomposition method that time-specializes both event features and image features for alignment. Specifically, we introduce a Relative Time-Encoded Attention module that steers event features toward motion information relevant to a given target time, and a Timesurface Dynamic Warping module that warps image features into the spatial configuration corresponding to that time. With time-specialized motion and image features explicitly aligned at arbitrary query times, our framework can decompose a single blurred image into a high-frame-rate sharp video sequence. In addition, we collect a new dataset containing real events and high-quality color videos, and synthesize blurred inputs by averaging sharp frames to evaluate our method. Experiments on multiple datasets with both synthetic and real events demonstrate that our approach consistently outperforms previous state-of-the-art methods on the blur decomposition task.