1. Compressed sensing with linear-in-wavenumber sampling in spectral-domain optical coherence tomography

    Compressed sensing with linear-in-wavenumber sampling in spectral-domain optical coherence tomography

    We propose a novel method called compressed sensing with linear-in-wavenumber sampling (k -linear CS) to retrieve an image for spectral-domain optical coherence tomography (SD-OCT). An array of points that is evenly spaced in wavenumber domain is sampled from an original interferogram by a preset k -linear mask. Then the compressed sensing based on l1 norm minimization is applied on these points to reconstruct an A-scan data. To get an OCT image, this method uses less than 20% of the total data as required in the typical process and gets rid of the spectral calibration with numerical interpolation in traditional CS-OCT ...

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