Rank-Regularized Measurement Operators for Compressive Imaging

Suhas Lohit, Rajhans Singh, Kuldeep Kulkarni, Pavan Turaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Compressive imaging is used to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference with purely data-driven models using deep learning. Although random projection has some advantages, we can get improved performance by learning the multiplexing patterns, also known as the measurement operator/matrix. However, at the time of training, it is not clear what the number of measurements should be. In this paper, we answer the following important question: How can we find the optimal number of measurements as well as the measurement matrix that can maintain a high-level of performance? Given the cost per measurement, our solution is to use regularization functions to encourage low-rank solutions for the learned measurement operator. We demonstrate that our solutions are effective on both image recognition and reconstruction problems.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781728143002
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove


  • Compressive imaging
  • deep learning
  • neural networks
  • nuclear norm
  • rank regularization

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


Dive into the research topics of 'Rank-Regularized Measurement Operators for Compressive Imaging'. Together they form a unique fingerprint.

Cite this