De Castro Mota, JF;
Weizman, L;
Deligiannis, N;
Eldar, Y;
Rodrigues, M;
(2016)
Reference-based compressed sensing: a sample complexity approach.
In:
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Proceedings.
(pp. pp. 4687-4691).
IEEE: Shanghai.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Reference-based compressed sensing: a sample complexity approach |
| Event: | IEEE International Conference on Acoustics, Speech and Signal Processing |
| ISBN-13: | 978-1-4799-9988-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP.2016.7472566 |
| Publisher version: | http://dx.doi.org/10.1109/ICASSP.2016.7472566 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | compressed sensing; minimisation; signal reconstruction; compressive video; linear measurements; medical imaging; reference based compressed sensing; reweighted minimization scheme; sparse signal reconstruct; Complexity theory; Compressed sensing; Electrical engineering; Image reconstruction; Minimization; Sparse matrices; Weight measurement; Compressed sensing; prior information; reweighted ℓ1 minimization; sample complexity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1529230 |
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