Open AccessThe Asymptotic Noise Distribution in Karhunen-Loeve Transform Eigenmodes
Author(s) -
Yu Ding,
Hui Xue,
Ning Jin,
YiuCho Chung,
Xin Liu,
Yongqin Zhang,
Orlando P. Simonetti
Publication year - 2013
Publication title -
journal of health and medical informatics
Language(s) - English
Resource type - Journals
ISSN - 2157-7420
DOI - 10.4172/2157-7420.1000122
Subject(s) - noise (video) , mathematics , karhunen–loève theorem , distribution (mathematics) , filter (signal processing) , statistical physics , random matrix , value noise , stochastic resonance , noise reduction , acoustics , noise measurement , statistics , mathematical analysis , algorithm , computer science , physics , noise floor , artificial intelligence , image (mathematics) , computer vision , eigenvalues and eigenvectors , quantum mechanics
Karhunen-Loeve Transform (KLT) is widely used in signal processing. Yet the well-accepted result is that, the noise is uniformly distributed in all eigenmodes is not accurate. We apply a result of the random matrix theory to understand the asymptotic noise distribution in KLT eigenmodes. Noise variances in noise-only eigenmodes follow the Marcenko-Pastur distribution, while noise variances in signal-dominated eigenmodes still follow the uniform distribution. Both the mathematical expectation of noise level in each eigenmode and an analytical formula of KLT filter noise reduction effect with a hard threshold were derived. Numerical simulations agree with our theoretical analysis. The noise variance of an eigenmode may deviate more than 60% from the uniform distribution. These results can be modified slightly, and generalized to non-IID (independently and identically-distributed) noise scenario. Magnetic resonance imaging experiments show that the generalized result is applicable and accurate. These generic results can help us understand the noise behavior in the KLT and related topics.