STB-VMM：基于Swin Transformer的视频运动放大
STB-VMM: Swin Transformer Based Video Motion Magnification
摘要：
视频运动放大技术的目标是放大视频中的小运动，以揭示先前不可见或不可见的运动。它的用途从生物医学应用和deepfake检测扩展到结构模态分析和预测性维护。然而，从噪声中辨别小的运动是一项复杂的任务，特别是当试图放大非常细微的、通常是亚像素的运动时。结果，运动放大技术通常遭受噪声和模糊输出。这项工作提出了一个新的国家的最先进的模型的基础上Swin的Transformer，它提供了更好的容忍噪声输入以及更高质量的输出，表现出更少的噪音，模糊，和文物比现有技术的输出图像质量的改进将使更精确的测量任何应用依赖于放大的视频序列，并且可以在新的技术领域中实现视频运动放大技术的进一步发展。
英文摘要：
The goal of video motion magnification techniques is to magnify small motions in a video to reveal previously invisible or unseen movement. Its uses extend from bio-medical applications and deepfake detection to structural modal analysis and predictive maintenance. However, discerning small motion from noise is a complex task, especially when attempting to magnify very subtle, often sub-pixel movement. As a result, motion magnification techniques generally suffer from noisy and blurry outputs. This work presents a new state-of-the-art model based on the Swin Transformer, which offers better tolerance to noisy inputs as well as higher-quality outputs that exhibit less noise, blurriness, and artifacts than prior-art. Improvements in output image quality will enable more precise measurements for any application reliant on magnified video sequences, and may enable further development of video motion magnification techniques in new technical fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
