Two-Dimensional Tomography from Noisy Projections Taken at Unknown Random Directions

DOI10.1137/090764657zbMath1279.68339OpenAlexW2023085305WikidataQ37593490 ScholiaQ37593490MaRDI QIDQ2873192

Hau-Tieng Wu, Amit Singer

Publication date: 23 January 2014

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3929313

zbMATH Keywords

Wiener filteringprincipal component analysiscomputerized tomographydiffusion mapssmall world graphJaccard indexgraph denoisingShepp-Logan phantom

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Computing methodologies for image processing (68U10) Radon transform (44A12)

Related Items (9)

Optimal prediction in the linearly transformed spiked model ⋮ Cohomology of Cryo-Electron Microscopy ⋮ Discrete tomography with unknown intensity levels using higher-order statistics ⋮ Think globally, fit locally under the manifold setup: asymptotic analysis of locally linear embedding ⋮ Moment-based angular difference estimation between two tomographic projections in 2D and 3D ⋮ Graph connection Laplacian methods can be made robust to noise ⋮ Embeddings of Riemannian manifolds with finite eigenvector fields of connection Laplacian ⋮ Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes ⋮ The Steerable Graph Laplacian and its Application to Filtering Image Datasets

This page was built for publication: Two-Dimensional Tomography from Noisy Projections Taken at Unknown Random Directions