We address the problem of reference-based compressed sensing: reconstructa sparse signal from few linear measurements using asprior information a reference signal, a signal similar to the signalwe want to reconstruct. Access to reference signals arises in applicationssuch as medical imaging, e.g., through prior images ofthe same patient, and compressive video, where previously reconstructedframes can be used as reference. Our goal is to use thereference signal to reduce the number of required measurements forreconstruction. We achieve this via a reweighted â„“1-â„“1 minimizationscheme that updates its weights based on a sample complexitybound. The scheme is simple, intuitive and, as our experimentsshow, outperforms prior algorithms, including reweighted â„“1 minimization,â„“1-â„“1 minimization, and modified CS.
Mota, J, Weizman, L, Deligiannis, N, Eldar, Y & Rodrigues, M 2016, Reference-based compressed sensing: A sample complexity approach. in IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2016. pp. 1-5, IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2016., 20/03/17.
Mota, J., Weizman, L., Deligiannis, N., Eldar, Y., & Rodrigues, M. (2016). Reference-based compressed sensing: A sample complexity approach. In IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2016 (pp. 1-5)
@inproceedings{0faf84016b1b479c8cd984f45ed854d8,
title = "Reference-based compressed sensing: A sample complexity approach",
abstract = "We address the problem of reference-based compressed sensing: reconstructa sparse signal from few linear measurements using asprior information a reference signal, a signal similar to the signalwe want to reconstruct. Access to reference signals arises in applicationssuch as medical imaging, e.g., through prior images ofthe same patient, and compressive video, where previously reconstructedframes can be used as reference. Our goal is to use thereference signal to reduce the number of required measurements forreconstruction. We achieve this via a reweighted â„“1-â„“1 minimizationscheme that updates its weights based on a sample complexitybound. The scheme is simple, intuitive and, as our experimentsshow, outperforms prior algorithms, including reweighted â„“1 minimization,â„“1-â„“1 minimization, and modified CS.",
keywords = "Compressed sensing, reweighted â„“1 minimization, prior information, sample complexity",
author = "Jo{\~a}o Mota and Lior Weizman and Nikolaos Deligiannis and Yonina Eldar and Miguel Rodrigues",
year = "2016",
language = "English",
pages = "1--5",
booktitle = "IEEE International Conference on Acoustics, Speech, and Signal Processing",
note = "IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2016. : ICASSP ; Conference date: 20-03-2017 Through 25-03-2017",
}