@inproceedings{ef0852185dc94af9b0eaab8eee9bbfee,
title = "Separable optimization for joint blind deconvolution and demixing",
abstract = "Blind deconvolution and demixing is the problem of reconstructing convolved signals and kernels from the sum of their convolutions. This problem arises in many applications, such as blind MIMO. In this work, we present a separable approach to blind deconvolution and demixing via convex optimization. Unlike previous works, our formulation allows separation into smaller optimization problems, allowing significantly improved complexity. We demonstrate the near optimal performance of our method, in accordance with the theoretical guarantees of the original, non-separable problem, under several normalization constraints.",
keywords = "Blind deconvolution, Demixing, Low-rank",
author = "Dana Weitzner and Raja Giryes",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9054464",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5989--5993",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
address = "الولايات المتّحدة",
}