Back to articles
Volume: 30 | Article ID: art00008
1-Bit Tensor Completion
  DOI :  10.2352/ISSN.2470-1173.2018.13.IPAS-261  Published OnlineJanuary 2018

Higher-order tensor structured data arise in many imaging scenarios, including hyperspectral imaging and color video. The recovery of a tensor from an incomplete set of its entries, known as tensor completion, is crucial in applications like compression. Furthermore, in many cases observations are not only incomplete, but also highly quantized. Quantization is a critical step for high dimensional data transmission and storage in order to reduce storage requirements and power consumption, especially for energy-limited systems. In this paper, we propose a novel approach for the recovery of low-rank tensors from a small number of binary (1-bit) measurements. The proposed method, called 1-bit Tensor Completion, relies on the application of 1-bit matrix completion over different matricizations of the underlying tensor. Experimental results on hyperspectral images demonstrate that directly operating with the binary measurements, rather than treating them as real values, results in lower recovery error.

Subject Areas :
Views 46
Downloads 7
 articleview.views 46
 articleview.downloads 7
  Cite this article 

Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides, "1-Bit Tensor Completionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVI,  2018,  pp 261-1 - 261-6,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2018
Electronic Imaging
Society for Imaging Science and Technology