Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration

Published

Conference Paper

© 2018 IEEE. Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor analysis for tensors generated by grouped image patches. The low-rank tensors are fed into the alternative direction multiplier method (ADMM) to further improve image reconstruction. The motivating application is compressive sensing (CS), and a deep convolutional architecture is adopted to approximate the expensive matrix inversion in CS applications. An iterative algorithm based on this low-rank tensor factorization strategy, called NLR-TFA, is presented in detail. Experimental results on noiseless and noisy CS measurements demonstrate the superiority of the proposed approach, especially at low CS sampling rates.

Full Text

Duke Authors

Cited Authors

  • Zhang, X; Yuan, X; Carin, L

Published Date

  • December 14, 2018

Published In

Start / End Page

  • 8232 - 8241

International Standard Serial Number (ISSN)

  • 1063-6919

International Standard Book Number 13 (ISBN-13)

  • 9781538664209

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2018.00859

Citation Source

  • Scopus