Snowflake Removal for Videos via Global and Local Low-Rank Decomposition

Abstract

Falling snow not only blocks human vision, but also significantly degrades the effectiveness of computer vision systems in outdoor environment. In this paper, we aim to remove snowflakes in videos by using the global and local low-rank property of snowflake-removed scenes. The stationary background and the mixture of moving foreground as well as falling snowflake are extracted via the global low-rank matrix decomposition. Some snowflake features, such as its color and size, are used to separate out the snowflakes from other moving objects. Then, the mean absolute difference based patch matching is applied to align every same moving object over frames to grab its low-rank structure. As such, the falling snowflake in front of moving objects can be removed via the local low-rank decomposition. Finally, the snowflake removed videos are generated by pasting moving foreground to stationary backgrounds. Experiments show that our method can remove snowflakes effectively and outperforms the comparison methods.