@inproceedings{09e1f89086064ce9bbf8f37f0227913c,
title = "Experimental scheme and restoration algorithm of block compression Sensing",
abstract = "Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.",
keywords = "Block compressive sensing, High resolution imaging, Reconstruction algorithm",
author = "Linxia Zhang and Qun Zhou and Jun Ke",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, OIT 2017 ; Conference date: 28-10-2017 Through 30-10-2017",
year = "2018",
doi = "10.1117/12.2295568",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Guohai Situ and Wolfgang Osten and Xun Cao",
booktitle = "2017 International Conference on Optical Instruments and Technology",
address = "United States",
}