论文标题
视频DeNoising的光流算法的定性研究
A qualitative investigation of optical flow algorithms for video denoising
论文作者
论文摘要
在媒体行业，工业检查和汽车等应用领域采用的许多视频分析和恢复算法中，良好的光流估计至关重要。在这项工作中，我们研究了当整合到最先进的视频Denoising算法中时，光流算法的性能很好。经典的光流算法（例如TV-L1）以及最近的基于深度学习的算法（例如RAFT或BMBC）。对于定性调查，我们将使用具有挑战性的特征（嘈杂的内容，大型运动等），而不是大多数出版物中使用的标准图像。
A good optical flow estimation is crucial in many video analysis and restoration algorithms employed in application fields like media industry, industrial inspection and automotive. In this work, we investigate how well optical flow algorithms perform qualitatively when integrated into a state of the art video denoising algorithm. Both classic optical flow algorithms (e.g. TV-L1) as well as recent deep learning based algorithm (like RAFT or BMBC) will be taken into account. For the qualitative investigation, we will employ realistic content with challenging characteristic (noisy content, large motion etc.) instead of the standard images used in most publications.
