Name
Abstract | ||
|---|---|---|
Inspired by the robustness and efficiency of the capped nuclear norm, in this paper, we apply it to 3D tensor applications and propose a novel low-rank tensor completion method via tensor singular value decomposition (t-SVD) for image and video recovery. The proposed tensor capped nuclear norm model (TCNN) handles corrupted low-rank tensors by sparsity enhancement via truncating its partial singular values dynamically. We also develop a simple and efficient algorithm to solve the proposed nonconvex and nonsmooth optimization problem using the Majorization-Minimization (MM) framework. Since the proposed algorithm admits a closed-form solution by optimizing a well-selected approximate version of the original objective function at each iteration, it is very efficient. Experimental results on both synthetic and real-world datasets, clearly demonstrate the superior performance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2934482 | IEEE ACCESS |
Keywords | DocType | Volume |
Low-rank tensor completion, tensor singular value decomposition, capped nuclear norm, visual data completion | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |