Nguyen, M. D., Tsiligianni, E., & Deligiannis, N. (2018). Deep learning sparse ternary projections for compressed sensing of images. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings: GlobalSIP 2017 (Vol. 2018-January, pp. 1125-1129). IEEE. https://doi.org/10.1109/GlobalSIP.2017.8309136

@inproceedings{46f7460e5dd942bfa3e7f7e9cb061a18,
title = "Deep learning sparse ternary projections for compressed sensing of images",
abstract = "Compressed sensing (CS) is a sampling theory that allows reconstructionof sparse (or compressible) signals from an incompletenumber of measurements, using of a sensing mechanism implementedby an appropriate projection matrix. The CS theory isbased on random Gaussian projection matrices, which satisfy recoveryguarantees with high probability; however, sparse ternary[0;-1;+1] projections are more suitable for hardware implementation.In this paper, we present a deep learning approach to obtainvery sparse ternary projections for compressed sensing. Our deeplearning architecture jointly learns a pair of a projection matrix and areconstruction operator in an end-to-end fashion. The experimentalresults on real images demonstrate the effectiveness of the proposedapproach compared to state-of-the-art methods, with significantadvantage in terms of complexity.",
keywords = "Compressed sensing, Deep learning, Sparse ternary projections",
author = "Nguyen, {Minh Duc} and Evangelia Tsiligianni and Nikolaos Deligiannis",
year = "2018",
month = mar,
day = "7",
doi = "10.1109/GlobalSIP.2017.8309136",
language = "English",
isbn = "978-1-5090-5990-4 ",
volume = "2018-January",
pages = "1125--1129",
booktitle = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",
publisher = "IEEE",
note = "IEEE Global Conference on Signal and Information Processing : GlobalSIP 2017 ; Conference date: 14-11-2017 Through 16-11-2017",
url = "https://2017.ieeeglobalsip.org",
}