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 deep fake 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.

本研究提出了一种基于 Swine Transformer 的视频运动放大技术的新模型，具有更好的对噪声输入的容忍性和输出高品质荧光的能力，可用于在任何依赖放大视频序列的应用程序中实现更精确的测量，为视频运动放大技术在新技术领域的进一步发展打下基础。