Assessment of robust reconstruction algorithms for compressive sensing spectral-domain optical coherence tomography

In this paper, we performed an in-depth assessment of current state-of-the-art compressive sensing (CS) reconstruction algorithms, including YALL1, CSALSA, NESTA, SPGL1, TwIST and SpaRSA for use in spectral domain optical coherence tomography (SD-OCT). A brief description of mentioned algorithms and criterion in assessing performance between constraint and unconstraint algorithms are presented. The performance of all algorithms is initially assessed using a set of artificial noiseless A-scan signals with different spatial-domain dynamic range. Reconstruction error, computation time, noise tolerance and reliability of each algorithm are used as key metrics. A fair speed comparison is then implemented. Finally, computation time, SNR and local contrast of the algorithms are evaluated on real OCT Bscan data. Our results show that SPGL1 and YALL1 have moderately better performance.

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