Compressed sensing with linear-in-wavenumber sampling in spectral-domain optical coherence tomography by Ning Zhang, Tiancheng Huo, Chengming Wang, Tianyuan Chen, Jing-gao Zheng, Ping Xue.
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 sampled from original interferogram by preset k-linear mask. Then the based on l1 norm minimization applied these reconstruct A-scan data. To get OCT image, this uses less than 20% total data as required typical process and gets rid spectral calibration numerical interpolation traditional CS-OCT. Therefore CS favorable high speed imaging. It demonstrated has same axial resolution performance ~30 dB higher signal-to-noise ratio (SNR) compared interpolation. Imaging bio-tissue SD-OCT also demonstrated.
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