Open Access
24 March 2018 Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation
Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani
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Abstract
We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches’ representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches’ representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2018/$25.00 © 2018 SPIE
Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, and Hossein Rabbani "Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation," Journal of Biomedical Optics 23(3), 036011 (24 March 2018). https://doi.org/10.1117/1.JBO.23.3.036011
Received: 12 January 2018; Accepted: 6 March 2018; Published: 24 March 2018
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CITATIONS
Cited by 42 scholarly publications.
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KEYWORDS
Optical coherence tomography

Image restoration

Denoising

Associative arrays

Reconstruction algorithms

Lawrencium

Visualization

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