  Video rain/snow removal from surveillance videos is an important task in the
computer vision community since rain/snow existed in videos can severely
degenerate the performance of many surveillance system. Various methods have
been investigated extensively, but most only consider consistent rain/snow
under stable background scenes. Rain/snow captured from practical surveillance
camera, however, is always highly dynamic in time with the background scene
transformed occasionally. To this issue, this paper proposes a novel rain/snow
removal approach, which fully considers dynamic statistics of both rain/snow
and background scenes taken from a video sequence. Specifically, the rain/snow
is encoded as an online multi-scale convolutional sparse coding (OMS-CSC)
model, which not only finely delivers the sparse scattering and multi-scale
shapes of real rain/snow, but also well encodes their temporally dynamic
configurations by real-time ameliorated parameters in the model. Furthermore, a
transformation operator imposed on the background scenes is further embedded
into the proposed model, which finely conveys the dynamic background
transformations, such as rotations, scalings and distortions, inevitably
existed in a real video sequence. The approach so constructed can naturally
better adapt to the dynamic rain/snow as well as background changes, and also
suitable to deal with the streaming video attributed its online learning mode.
The proposed model is formulated in a concise maximum a posterior (MAP)
framework and is readily solved by the ADMM algorithm. Compared with the
state-of-the-art online and offline video rain/snow removal methods, the
proposed method achieves better performance on synthetic and real videos
datasets both visually and quantitatively. Specifically, our method can be
implemented in relatively high efficiency, showing its potential to real-time
video rain/snow removal.
