@inproceedings{5f123a2c70e24046b7d157c3df434236,
title = "Deep learning approaches for sparse recovery in compressive sensing",
abstract = "Compressive sensing enables sparse signals recovery by less measurements than required by the Nyquist rate, so leading to energy and processing saving. Accuracy and complexity improvements can be achieved applying neural network to sparse linear inverse problem. This work focuses on sparse recovery with deep network. Improvements to the Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) and a novel neural network are proposed. Results show that these propositions can decrease up to 10.8 dB the NMSE value and require fewer layers than if only LISTA is used to estimate the signal.",
keywords = "Compressive Sensing, Deep Network, Learned Iterative Shrinkage-Thresholding Algorithm, Sparse Recovery",
author = "Marques, \{E. C.\} and N. Maciel and L. Naviner and H. Cai and J. Yang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th International Symposium on Image and Signal Processing and Analysis, ISPA 2019 ; Conference date: 23-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
day = "1",
doi = "10.1109/ISPA.2019.8868841",
language = "English",
series = "International Symposium on Image and Signal Processing and Analysis, ISPA",
publisher = "IEEE Computer Society",
pages = "129--134",
editor = "Sven Loncaric and Robert Bregovic and Marco Carli and Marko Subasic",
booktitle = "ISPA 2019 - 11th International Symposium on Image and Signal Processing and Analysis",
}