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

Content Provider	IET Digital Library
Author	S., Baiju P. P., Deepak Jayan George, Sudhish N.
Abstract	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.
Starting Page	3602
Ending Page	3612
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1409
Journal	IET Image Processing
Publisher Date	2020-09-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Computer Vision Computer Vision Algorithm Computer Vision And Image Processing Technique Image Motion Analysis Image Representation Image Segmentation Image Sequence Inclement Condition Low Rankness Low-rank Approximation Framework Low-rank Tensor Recovery Minimisation Normal Weather Condition Optical, Image And Video Signal Processing Optimisation Technique Original Rain-free Data Outdoor Monitoring System Rain Rainy Condition Singular Value Decomposition Sparse Rain Streaks Suitable Algorithm Tensor Singular Value Decomposition Tensor Total Variation Tensors TTV Regularisation Facilitates Unpleasant Rainy Effect Video Deraining Video Footage Video Frames Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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