A Novel Algorithm for Deraining Videos Using Range-Domain Filtering with Spatio-Temporal Correspondence

Unconstrained environment has always been a challenge to handle in computer vision and outdoor video analytics. Not only the visual perception but also the algorithmic efficiency at outdoor environment vastly impacted due to adverse weather conditions like rain, fog, snow, etc. These weather conditions largely degrade video quality by inducing complex noise into video frames especially due to mutual motion between capture assembly/ camera and object/ scene of interest. This greatly hampers video analytics and computer vision in various real time applications such as intelligent transportation systems, smart surveillance etc. Hence, restoring clean video from adverse effect of environment is one of the most important challenge in video analytics. With the aim of tackling this problem this paper proposes an efficient method of deraining videos by applying bilateral filtering on time-sliced video frames. The proposed idea exploited the strength of range-domain filtering possessed by bilateral filters and the spatio-temporal model of time-slicing. Extensive experimentation and their quantitative results indicate that our approach is reasonably effective in deraining video frames under different mutual motion between camera and object of interest in different rain density and is comparable to the state-of-the-art models existing in this domain and outperforms existing approaches with respect to performance time.