Abstract In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4-D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11
Zhang et al. REF implemented novel methods for multi-linear data completion based on t-SVD through a tensor nuclear norm penalized algorithm for video completion from missing entries.
Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD
2014 IEEE Conference on Computer Vision and Pattern Recognition
2014 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science