Sparsity based denoising of spectral domain optical coherence tomography images.


Journal Article

In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online.

Full Text

Duke Authors

Cited Authors

  • Fang, L; Li, S; Nie, Q; Izatt, JA; Toth, CA; Farsiu, S

Published Date

  • May 1, 2012

Published In

Volume / Issue

  • 3 / 5

Start / End Page

  • 927 - 942

PubMed ID

  • 22567586

Pubmed Central ID

  • 22567586

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.3.000927


  • eng

Conference Location

  • United States