Fast acquisition and reconstruction of optical coherence tomography images via sparse representation.

Journal Article (Journal Article)

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.

Full Text

Duke Authors

Cited Authors

  • Fang, L; Li, S; McNabb, RP; Nie, Q; Kuo, AN; Toth, CA; Izatt, JA; Farsiu, S

Published Date

  • November 2013

Published In

Volume / Issue

  • 32 / 11

Start / End Page

  • 2034 - 2049

PubMed ID

  • 23846467

Pubmed Central ID

  • PMC4000559

Electronic International Standard Serial Number (EISSN)

  • 1558-254X

Digital Object Identifier (DOI)

  • 10.1109/TMI.2013.2271904


  • eng

Conference Location

  • United States