Abstract
Videos captured by outdoor surveillance equipments
sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks. Rain streak
removal from a video is thus an important topic in recent
computer vision research. In this paper, we raise two intrinsic characteristics specifically possessed by rain streaks. Firstly, the rain streaks in a video contain repetitive local
patterns sparsely scattered over different positions of the
video. Secondly, the rain streaks are with multiscale configurations due to their occurrence on positions with different
distances to the cameras. Based on such understanding, we
specifically formulate both characteristics into a multiscale
convolutional sparse coding (MS-CSC) model for the video
rain streak removal task. Specifically, we use multiple convolutional filters convolved on the sparse feature maps to
deliver the former characteristic, and further use multiscale
filters to represent different scales of rain streaks. Such a
new encoding manner makes the proposed method capable
of properly extracting rain streaks from videos, thus getting
fine video deraining effects. Experiments implemented on
synthetic and real videos verify the superiority of the proposed method, as compared with the state-of-the-art ones
along this research line, both visually and quantitatively