Automatic hard thresholding for sparse signal reconstruction from NDE measurementsAIP Conference Proceedings
Document TypeConference Proceeding
AbstractWe propose an automatic hard thresholding (AHT) method for sparse‐signal reconstruction. The measurements follow an underdetermined linear model, where the regression‐coefficient vector is modeled as a superposition of an unknown deterministic sparse‐signal component and a zero‐mean white Gaussian component with unknown variance. Our method demands no prior knowledge about signal sparsity. Our AHT scheme approximately maximizes a generalized maximum likelihood (GML) criterion, providing an approximate GML estimate of the signal sparsity level and an empirical Bayesian estimate of the regression coefficients. We apply the proposed method to reconstruct images from sparse computerized tomography projections and compare it with existing approaches.
RightsCopyright 2010 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.
Copyright OwnerAmerican Institute of Physics
Citation InformationAleksandar Dogandžić and Kun Qiu. "Automatic hard thresholding for sparse signal reconstruction from NDE measurements" AIP Conference Proceedings Vol. 1211 (2010) p. 806 - 813
Available at: http://works.bepress.com/aleksandar_dogandzic/35/