Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
Name
1804.06128.pdf
Description
Submitted version
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
a01cb7a0a116866541ef284d84ebed26
Author(s)
Ko, Ching-Yun
Batselier, Kim
Yu, Wenjian
Wong, Ngai
Date Issued
August 2015
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ko, Ching-Yun et al. “Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains.” IEEE Transactions on Image Processing, 29 (August 2015) © 2015 The Author(s)
Version
Original manuscript
Abstract
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is modeled as a low-rank tensor train, where we use the known tensor entries and their coordinates to update the tensor train. A novel tensor train initialization procedure is proposed specifically for image and video completion, which is demonstrated to ensure fast convergence of the completion algorithm. The tensor train framework is also shown to easily accommodate Total Variation and Tikhonov regularization due to their low-rank tensor train representations. Image and video inpainting experiments verify the superiority of the proposed scheme in terms of both speed and scalability, where a speedup of up to 155\times is observed compared to state-of-the-art tensor completion methods at a similar accuracy. Moreover, we demonstrate the proposed scheme is especially advantageous over existing algorithms when only tiny portions (say, 1%) of the to-be-completed images/videos are known.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
MIT-IBM Watson AI Lab
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TIP.2020.2995061
