@inproceedings{145bf05055594aad9b8155aa4eaedc19,
title = "Distributed TV-L1 image fusion using PDMM",
abstract = "Distributed image fusion over networks has had little coverage in the literature, particularly considering the recent emergence of large networks of imaging sensors such as radio telescope arrays and wireless self-contained node networks. We present a fully asynchronous and distributed approach for image fusion in a general network with partially overlapping node fields of view. We use the example of an aerial surveillance drone network to present the advantages of our system and show that the communication power required for performing image fusion in-network is orders of magnitude lower than transmitting all raw images back to a distant central processor, while still achieving the same fusion performance.",
keywords = "distributed, fusion, image, networks, sensor",
author = "Matt O'Connor and Kleijn, \{W. Bastiaan\} and Thushara Abhayapala",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952772",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3326--3330",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}