Reference-based compressed sensing: A sample complexity approach

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

doi:10.1109/ICASSP.2016.7472566

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 proceeding › Conference contribution

17 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Publisher	IEEE
Pages	4687-4691
Number of pages	5
ISBN (Electronic)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472566
Publication status	Published - Jul 2016
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016

Conference

Conference	41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated title	ICASSP 2016
Country/Territory	China
City	Shanghai
Period	20/03/16 → 25/03/16

Keywords

Compressed sensing
prior information
reweighted ℓ1 minimization
sample complexity

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2016.7472566

Cite this

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title = "Reference-based compressed sensing: A sample complexity approach",

abstract = "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.",

keywords = "Compressed sensing, prior information, reweighted ℓ1 minimization, sample complexity",

author = "Mota, {Jo{\~a}o F. C.} and Lior Weizman and Nikos Deligiannis and Eldar, {Yonina C.} and Rodrigues, {Miguel Raul Dias}",

year = "2016",

month = jul,

doi = "10.1109/ICASSP.2016.7472566",

language = "English",

pages = "4687--4691",

booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

}

Mota, JFC, Weizman, L, Deligiannis, N, Eldar, YC & Rodrigues, MRD 2016, Reference-based compressed sensing: A sample complexity approach. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings., 7472566, IEEE, pp. 4687-4691, 41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016, Shanghai, China, 20/03/16. https://doi.org/10.1109/ICASSP.2016.7472566

TY - GEN

T1 - Reference-based compressed sensing

T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016

AU - Mota, João F. C.

AU - Weizman, Lior

AU - Deligiannis, Nikos

AU - Eldar, Yonina C.

AU - Rodrigues, Miguel Raul Dias

PY - 2016/7

Y1 - 2016/7

N2 - 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.

AB - 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.

KW - Compressed sensing

KW - prior information

KW - reweighted ℓ1 minimization

KW - sample complexity

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M3 - Conference contribution

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BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings

PB - IEEE

Y2 - 20 March 2016 through 25 March 2016

ER -

Reference-based compressed sensing: A sample complexity approach

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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