We present a novel approach of leveraging deep learning to reconstruct high-resolution OCT B-scans from reduced axial resolution data. In this work, the original OCT signal is used as the ground truth, and lower resolution was simulated by windowing the interference fringes. A super-resolution pixel-to-pixel generative adversarial network (GAN) was investigated for reconstructing high-resolution OCT data in the spatial domain and is compared against reconstructing in the spectral domain.
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