Ma, S.; Fan, Y.; Fang, S.; Yang, W.; Li, L. Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme. Preprints2023, 2023101523. https://doi.org/10.20944/preprints202310.1523.v1
APA Style
Ma, S., Fan, Y., Fang, S., Yang, W., & Li, L. (2023). Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme. Preprints. https://doi.org/10.20944/preprints202310.1523.v1
Chicago/Turabian Style
Ma, S., Weichao Yang and Li Li. 2023 "Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme" Preprints. https://doi.org/10.20944/preprints202310.1523.v1
Abstract
Employing low tensor rank decompositions in image inpainting has attracted increasing attention. This paper exploits a novel tensor-augmentation schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed augmentation schemes enhance the low-rankness of an image under three tensor decompositions: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solve the image inpainting problem with three low-rank con-strained models which use the matrix rank, TT rank, and tubal rank as constrained priors re-spectively. The tensor tubal rank and tensor train multi-rank are developed from t-SVD and TT decomposition respectively. We exploit efficient ADMM algorithms for solving the three models. Experimental results demonstrate that our methods are effective for image inpainting and supe-rior to numerous close methods.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
