Review of Progress in Quantitative Nondestructive Evaluation
We 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.
American Institute of Physics
Dogandžić, Aleksandar and Qiu, Kun, "Automatic hard thresholding for sparse signal reconstruction from NDE measurements" (2009). Center for Nondestructive Evaluation Conference Papers, Posters and Presentations. 62.