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Proceedings Paper

Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography
Author(s): Xuan Liu; Jin U. Kang
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Paper Abstract

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD-OCT). Namely, CS was applied to the spectral data in reconstructing A-mode images. This would eliminate the need for a large amount of spectral data for image reconstruction and processing. We tested the CS method by randomly undersampling k-space SD-OCT signal. OCT images are reconstructed by solving an optimization problem that minimizes the l1 norm to enforce sparsity, subject to data consistency constraints. Variable density random sampling and uniform density random sampling were studied and compared, which shows the former undersampling scheme can achieve accurate signal recovery using less data.

Paper Details

Date Published: 11 February 2011
PDF: 7 pages
Proc. SPIE 7904, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII, 79041C (11 February 2011); doi: 10.1117/12.874058
Show Author Affiliations
Xuan Liu, The Johns Hopkins Univ. (United States)
Jin U. Kang, The Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 7904:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII
Jose-Angel Conchello; Carol J. Cogswell; Tony Wilson; Thomas G. Brown, Editor(s)

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