Reference-based compressed sensing: A sample complexity approach

João F. C. Mota, Lior Weizman, Nikos Deligiannis, Yonina C. Eldar, Miguel Raul Dias Rodrigues

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

17 Citations (Scopus)


We address the problem of reference-based compressed sensing: reconstruct a sparse signal from few linear measurements using as prior information a reference signal, a signal similar to the signal we want to reconstruct. Access to reference signals arises in applications such as medical imaging, e.g., through prior images of the same patient, and compressive video, where previously reconstructed frames can be used as reference. Our goal is to use the reference signal to reduce the number of required measurements for reconstruction. We achieve this via a reweighted ℓ1-ℓ1 minimization scheme that updates its weights based on a sample complexity bound. The scheme is simple, intuitive and, as our experiments show, outperforms prior algorithms, including reweighted ℓ1 minimization, ℓ1-ℓ1 minimization, and modified CS.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Number of pages5
ISBN (Electronic)9781479999880
Publication statusPublished - Jul 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016


Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016


  • Compressed sensing
  • prior information
  • reweighted ℓ1 minimization
  • sample complexity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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