Blind Deconvolution Meets Blind Demixing: Algorithms and Performance Bounds
نویسندگان
چکیده
منابع مشابه
Blind Signal Deconvolution by Spatio Temporal Decorrelation and Demixing
In this paper we present a simple efficient local unsupervised learning algorithm for on-line adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolutive mixtures. Based on the existence of the inverse filter, we construct a two-stage neural netwo...
متن کاملPerformance bounds for linear blind and group-blind multiuser detectors
In blind multiuser detection for CDMA systems, the receiver knows only the code of the user of interest, while in group-blind multiuser detection the receiver knows a subset of codes, e.g., the in-cell users in a basestation. This paper derives bounds for the performance of linear blind and group-blind multiuser detectors. The bounds are derived under a number of different system assumptions. T...
متن کاملRegularized Gradient Descent: A Nonconvex Recipe for Fast Joint Blind Deconvolution and Demixing
We study the question of extracting a sequence of functions {fi, gi}i=1 from observing only the sum of their convolutions, i.e., from y = ∑s i=1 fi ∗ gi. While convex optimization techniques are able to solve this joint blind deconvolution-demixing problem provably and robustly under certain conditions, for medium-size or large-size problems we need computationally faster methods without sacrif...
متن کاملLight Field Blind Deconvolution
We address for the first time the issue of motion blur in light field images captured from plenoptic cameras (instead of camera arrays), where the spatial sampling in each view is decimated. We propose a solution to the estimation of a sharp light field given a blurry one, when the motion blur point spread function is unknown, i.e., the so-called blind deconvolution problem. Unfortunately, the ...
متن کاملLearning Blind Deconvolution
In this work, we propose a novel prior term for the regularization of blind deblurring methods. The proposed method introduces machine learning techniques into the blind deconvolution process. The proposed technique has sound mathematical foundations and is generic to many inverse problems. We demonstrate the usage of this regularizer within Bayesian blind deconvolution framework, and also inte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2017
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2017.2701342