Tensor total variation regularised low-rank approximation framework for video deraining

Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy conditions comprises several visual distortions. It eventually leads to flaws when handled with succeeding computer vision algorithms, namely the object identification and tracking. Additionally, eliminating such unpleasant rainy effects is essential prior to the processing of video footage by suitable algorithms. The present work attempts to formulate a new low-rank tensor recovery based deraining algorithm that enables to remove the rain streaks from video footage. The proposed method detects the rain streaks by adopting optical flow estimation along with the brightness features inherent with the rain streaks. A unified framework comprised of tensor singular value decomposition (t-SVD) based weighted nuclear norm minimisation and tensor total variation (TTV) regularisation effectively removes rain streaks and recovers the original rain-free data from the available rainy data. The use of t-SVD enforces the concept of low rankness and also exploits the temporal redundancy among the video frames. Furthermore, TTV regularisation facilitates to promote the temporal continuity for discriminating most of the natural image contents from sparse rain streaks by preserving piece-wise smoothness of video frames. Comprehensive experimental findings based on real and synthetic data with dynamic background show that the rain streaks are more efficaciously eliminated by adopting the proposed method without much loss in the information.