Journal
BIOMEDICAL OPTICS EXPRESS
Volume 3, Issue 5, Pages 927-942Publisher
OPTICAL SOC AMER
DOI: 10.1364/BOE.3.000927
Keywords
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Funding
- American Health Assistance Foundation
- NIH [1R21EY021321-01A1]
- National Natural Science Foundation of China [61172161]
- Chinese Ministry of Education
- Hunan University
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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. (c) 2012 Optical Society of America
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